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                  <text>VOL. 18, NO. 3 2020</text>
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                <text>TELKOMNIKA Telecommunication, Computing, Electronics and Control &#13;
Web-app realization of Shor’s quantum factoring algorithm and Grover’s quantum search algorithm </text>
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                <text>Grover, Quantum application, Shor, Web-app</text>
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                <text>Quantum algorithms are well-known for their quadratic if not exponential speedup over their classical counterparts. The two widely-known quantum algorithms are Shor’s quantum factoring algorithm and Grover’s quantum search algorithm. Shor’s quantum factoring algorithm could perform integer factorization in O(logN). Grover’s quantum search algorithm could solve the unsorted search problem in O(√N). However, both algorithms are introduced as theoretical concepts in the original papers due to the limitations of quantum technology at that time. In this paper, an improved way is presented to realize the two algorithms into a web application using state-of-the-art quantum technology. The web-app is designed and built considering the uses of a quantum simulator and libraries provided by ProjectQ and Rigetti Forest. The result shows&#13;
that both algorithms are realizable into web-applications. </text>
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                <text>Arya Wicaksana, Anthony, Adjie Wahyu Wicaksono</text>
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                <text>DOI: 10.12928/TELKOMNIKA.v18i3.14755</text>
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                <text>Universitas Ahmad Dahlan</text>
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                <text>Sri Wahyuni</text>
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                <text>ISSN: 1693-6930</text>
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                <text>TELKOMNIKA Telecommunication, Computing, Electronics and Control </text>
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                  <text>VOL. 18, NO. 3 2020</text>
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                <text>TELKOMNIKA Telecommunication, Computing, Electronics and Control &#13;
UNet-VGG16 with transfer learning for MRI-based brain tumor segmentation </text>
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                <text>Fully convolution network, Image segmentation, Transfer learning, U-Net, VGG16</text>
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                <text>A brain tumor is one of a deadly disease that needs high accuracy in its&#13;
medical surgery. Brain tumor detection can be done through magnetic&#13;
resonance imaging (MRI). Image segmentation for the MRI brain tumor aims to separate the tumor area (as the region of interest or ROI) with a healthy brain and provide a clear boundary of the tumor. This study classifies the ROI and non-ROI using fully convolutional network with new architecture, namely UNet-VGG16. This model or architecture is a hybrid of U-Net and VGG16 with transfer Learning to simplify the U-Net architecture. This method has a high accuracy of about 96.1% in the learning dataset. The validation is done by calculating the correct classification ratio (CCR) to comparing the segmentation result with the ground truth. The CCR value shows that this UNet-VGG16 could recognize the brain tumor area with a mean of CCR value is about 95.69%. </text>
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                <text>Anindya Apriliyanti Pravitasari, Nur Iriawan, Mawanda Almuhayar, Taufik Azmi, Irhamah, Kartika Fithriasari, Santi Wulan Purnami, Widiana Ferriastuti</text>
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                <text>DOI: 10.12928/TELKOMNIKA.v18i3.14753</text>
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                <text>Universitas Ahmad Dahlan</text>
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                <text>Sri Wahyuni</text>
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                <text>ISSN: 1693-6930</text>
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                <text>TELKOMNIKA Telecommunication, Computing, Electronics and Control </text>
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                  <text>VOL. 18, NO. 3 2020</text>
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                <text>TELKOMNIKA Telecommunication, Computing, Electronics and Control &#13;
Classification of pneumonia from X-ray images using siamese convolutional network </text>
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                <text>Chest X-Ray, Image classification, Pneumonia, Siamese Convolutional, Network</text>
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                <text>Pneumonia is one of the highest global causes of deaths especially for children under 5 years old. This happened mainly because of the difficulties in identifying the cause of pneumonia. As a result, the treatment given may not be suitable for each pneumonia case. Recent studies have used deep learning approaches to obtain better classification within the cause of pneumonia. In this research, we used&#13;
siamese convolutional network (SCN) to classify chest x-ray pneumonia image into 3 classes, namely normal conditions, bacterial pneumonia, and viral pneumonia. Siamese convolutional network is a neural network architecture that learns similarity knowledge between pairs of image inputs based on the differences between its features. One of the important benefits of classifying data with SCN is the availability of comparable images that can be used as a reference when determining class. Using SCN, our best model achieved 80.03% accuracy, 79.59% f1 score, and an improved result reasoning by providing the comparable images. </text>
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                <text>Kennard Alcander Prayogo, Alethea Suryadibrata, Julio Christian Young</text>
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                <text>DOI: 10.12928/TELKOMNIKA.v18i3.14751</text>
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                <text>Universitas Ahmad Dahlan</text>
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                <text>Sri Wahyuni</text>
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                <text>ISSN: 1693-6930</text>
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                <text>TELKOMNIKA Telecommunication, Computing, Electronics and Control </text>
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        <name>Siamese Convolutional</name>
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                  <text>VOL. 18, NO. 3 2020</text>
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                <text>TELKOMNIKA Telecommunication, Computing, Electronics and Control &#13;
Contour evolution method for precise boundary delineation of medical images </text>
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                <text>Image segmentation is an important precursor to boundary delineation&#13;
of medical images. One of the major challenges in applying automatic image segmentation in medical images is the imperfection in the imaging process which can result in inconsistent contrast and brightness levels, and low image sharpness and vanishing boundaries. Although recent advances in deep learning produce vast improvements in the quality of image segmentation, the accuracy of segmentation around object boundaries still requires improvement. We developed a new approach to contour evolution that is more intuitive but shares some common principles with the active contour model method. The method uses two concepts, namely the boundary grid and sparse boundary representation, as an implicit and explicit representation of the boundary points. We tested our method using lumbar spine MRI images of 515 patients. The experiment results show that our method performs up to 10.2 times faster and more flexible than the geodesic active contours method. Using BF-score contour-based metric, we show that our method improves the boundary accuracy from 74% to 84% as opposed to 63% by the latter method. </text>
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                <text>Friska Natalia, Hira Meidia, Nunik Afriliana, Julio Christian Young, Sud Sudirman</text>
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                <text>DOI: 10.12928/TELKOMNIKA.v18i3.14746</text>
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                <text>Universitas Ahmad Dahlan</text>
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                <text>Sri Wahyuni</text>
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              <elementText elementTextId="39871">
                <text>ISSN: 1693-6930</text>
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                <text>TELKOMNIKA Telecommunication, Computing, Electronics and Control </text>
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Wireless communication system with frequency selective channel OFDM modulation technique </text>
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                <text>This paper introduces the design and implementation of a wireless&#13;
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The simulation results of the designed system as well as the frequency&#13;
equilizer showed a robustness against the frequency selective faded channel effects. The maximum obtained bit error rate was around 10-5, which means that original signal was effectively recovered. </text>
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              <elementText elementTextId="39842">
                <text>Hayder Al Ibraheemi, Mazen M. A. Al Ibraheemi</text>
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            <description>A related resource from which the described resource is derived</description>
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              <elementText elementTextId="39843">
                <text>DOI: 10.12928/TELKOMNIKA.v18i3.14683</text>
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            <name>Publisher</name>
            <description>An entity responsible for making the resource available</description>
            <elementTextContainer>
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                <text>Universitas Ahmad Dahlan</text>
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              <elementText elementTextId="39846">
                <text>Sri Wahyuni</text>
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            </elementTextContainer>
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          <element elementId="47">
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            <description>Information about rights held in and over the resource</description>
            <elementTextContainer>
              <elementText elementTextId="39847">
                <text>ISSN: 1693-6930</text>
              </elementText>
            </elementTextContainer>
          </element>
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              <elementText elementTextId="39848">
                <text>http://journal.uad.ac.id/index.php/TELKOMNIKA</text>
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                <text>TELKOMNIKA Telecommunication, Computing, Electronics and Control </text>
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                  <text>VOL. 18, NO. 3 2020</text>
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      <name>Text</name>
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                <text>TELKOMNIKA Telecommunication, Computing, Electronics and Control &#13;
Image denosing in underwater acoustic noise using discrete wavelet transform with different noise level estimation </text>
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            <description>The topic of the resource</description>
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                <text>Colored noise, Denoising, Estimation, PSNR, Wavelet transform</text>
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            <description>An account of the resource</description>
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                <text>In many applications, Image de-noising and improvement represent essential processes in presence of colored noise such that in underwater. Power spectral density of the noise is changeable within a definite frequency range, and autocorrelation noise function is does not like delta function. So, noise in underwater is characterized as colored noise. In this paper, a novel image de-noising method is proposed using multi-level noise power estimation in discrete wavelet transform with different basis functions. Peak signal to noise ratio (PSNR) and mean squared error represented performance measures that the results of this study depend on it. The results of various bases of wavelet such as: Daubechies (db), biorthogonal (bior.) and symlet (sym.), show that denoising process that uses in this method produces extra prominent images and improved values of PSNR than other methods. </text>
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            <description>An entity primarily responsible for making the resource</description>
            <elementTextContainer>
              <elementText elementTextId="39828">
                <text>Yasin Yousif Al-Aboosi, Radhi Sehen Issa, Ali khalid Jassim</text>
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            <description>A related resource from which the described resource is derived</description>
            <elementTextContainer>
              <elementText elementTextId="39829">
                <text>DOI: 10.12928/TELKOMNIKA.v18i3.14381</text>
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            </elementTextContainer>
          </element>
          <element elementId="45">
            <name>Publisher</name>
            <description>An entity responsible for making the resource available</description>
            <elementTextContainer>
              <elementText elementTextId="39830">
                <text>Universitas Ahmad Dahlan</text>
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              <elementText elementTextId="39832">
                <text>Sri Wahyuni</text>
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            </elementTextContainer>
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          <element elementId="47">
            <name>Rights</name>
            <description>Information about rights held in and over the resource</description>
            <elementTextContainer>
              <elementText elementTextId="39833">
                <text>ISSN: 1693-6930</text>
              </elementText>
            </elementTextContainer>
          </element>
          <element elementId="46">
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            <description>A related resource</description>
            <elementTextContainer>
              <elementText elementTextId="39834">
                <text>http://journal.uad.ac.id/index.php/TELKOMNIKA</text>
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              <elementText elementTextId="39835">
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              <elementText elementTextId="39836">
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            <description>The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant</description>
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              <elementText elementTextId="39838">
                <text>TELKOMNIKA Telecommunication, Computing, Electronics and Control </text>
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        <name>Estimation</name>
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            <element elementId="50">
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                  <text>VOL. 18, NO. 3 2020</text>
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          <element elementId="50">
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                <text>TELKOMNIKA Telecommunication, Computing, Electronics and Control &#13;
Comparison between piezoelectric transformer and electromagnetic transformer used in electronic circuitsv </text>
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            <description>The topic of the resource</description>
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                <text>Piezoelectric material, Piezoelectric transformer, PZT</text>
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                <text>This paper presents study, modeling and simulation of the piezoelectric&#13;
material works as transformer (piezoelectric transformer (PT)) in power&#13;
electronic circuits, comparisons are made with the regular transformer&#13;
(iron core) works in the same circuit, the tested circuit is the full bridge&#13;
converter which used in the simulation as dc power supply circuit. As a result, a detailed simulation for both the piezoelectric transformer and&#13;
traditional transformer are achieved, as well as the output voltage from the dc power supply is tested by varying the load resistance. The dc power supply circuit has been simulated using PSIM (V9.1) power electronic circuit simulation software. </text>
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                <text>Wedian Hadi Abd Al Ameer, Mustafa A. Fadel Al-Qaisi, Ammar Al-Gizi</text>
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            <elementTextContainer>
              <elementText elementTextId="39815">
                <text>DOI: 10.12928/TELKOMNIKA.v18i3.14334</text>
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            </elementTextContainer>
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            <description>An entity responsible for making the resource available</description>
            <elementTextContainer>
              <elementText elementTextId="39816">
                <text>Universitas Ahmad Dahlan</text>
              </elementText>
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              <elementText elementTextId="39817">
                <text>June 2020</text>
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              <elementText elementTextId="39818">
                <text>Sri Wahyuni</text>
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          <element elementId="47">
            <name>Rights</name>
            <description>Information about rights held in and over the resource</description>
            <elementTextContainer>
              <elementText elementTextId="39819">
                <text>ISSN: 1693-6930</text>
              </elementText>
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          <element elementId="46">
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              <elementText elementTextId="39820">
                <text>http://journal.uad.ac.id/index.php/TELKOMNIKA</text>
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                <text>English</text>
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Simple broadband circularly polarized monopole antenna with two asymmetrically connected U-shaped parasitic strips and defective ground plane </text>
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                <text>A simple compact broadband circularly polarized monopole antenna,&#13;
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                <text>Hussein Alsariera,  Z. Zakaria, A. A. M. Isa, Sameer Alani, M. Y. Zeain, Othman S. Al-Heety, S. Ahmed, Mussa Mabrok, R. Alahnomi</text>
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                <text>DOI: 10.12928/TELKOMNIKA.v18i3.14313</text>
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                <text>Sri Wahyuni</text>
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                <text>ISSN: 1693-6930</text>
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l A modified symmetric local binary pattern for image features extraction</text>
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                <text>The process of identifying images and patterns is one of the most important processes of digital image processing, which is used in many applications such as fingerprint recognition, face recognition and pattern recognition. Due to the large size of the image, the process of identifying the image requires a great time, which in turn leads us to extract some characteristics of the magnitude of the volume, which can be used as an identifier to retrieve the image or recognize it and thus we have devoted a lot of time to identify the image. In this research paper, a modified symmetric local binary pattern (MSLBP) method was proposed to extract texture features. The proposed algorithm was implemented on many digital fingerprint’s images and the local structure features of these images were obtained. Several image recognition experiments are conducted on these features and compared with other algorithms. The results of the proposed algorithm showed that the digital image was represented in a very small size and furthermore the speed and accuracy of image recognition based on the proposed method was increased significantly. Unlike the methods based on LBP, the proposed method gives the same features of the image even if the image was rotated&#13;
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                <text>DOI: 10.12928/TELKOMNIKA.v18i3.14256</text>
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                <text>Sri Wahyuni</text>
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                <text>ISSN: 1693-6930</text>
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Performance analysis of image transmission with various channel conditions/modulation techniques </text>
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                <text>This paper investigates the impact of different modulation techniques for digital communication systems that employ quadrature phase shift keying (QPSK) and quadrature amplitude modulation (16-QAM and 64-QAM) to transmit images over AWGN and Rayleigh fading channels for the cellular mobile networks. In the further steps, wiener and median filters has been adopted to the simulation are used at the receiver side to remove the impulsive noise present in the received image. This work is performed to evaluate the transmission of two dimensional (2D) gray-scale and color-scale (RGB) images with different values from signal to noise ratios (SNR), such as; (5, 10 and 15) dB over different channels. The correct conclusions are made by comparing many of the observed Matlab simulation results. This was carried out through the results that measure the quality of received image, which is analyzes in terms of SNRimage peak signal to noise ratio (PSNR) and mean square error (MSE). </text>
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                <text>DOI: 10.12928/TELKOMNIKA.v18i3.14172</text>
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