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<!DOCTYPE html>
<html >
<head>
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<meta name="description" content="National Chung Cheng University, Department of Computer Science and Information Engineering, Machine Vision and Learning Lab, CCU MVL LAB, 江振國">
<title>Machine Vision and Learning Lab</title>
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Machine Vision and Learning Lab </a></span>
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<ul class="navbar-nav nav-dropdown nav-right" data-app-modern-menu="true"><li class="nav-item dropdown">
<a class="nav-link link text-white display-7" href="index.html#header1-b">HOME</a>
</li><li class="nav-item"><a class="nav-link link text-white display-7" href="index.html#header16-k">RESEARCH</a></li><li class="nav-item"><a class="nav-link link text-white display-7" href="index.html#testimonials1-v">MEMBERS</a></li>
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<a class="nav-link link text-white display-7" href="index.html#contacts1-s">CONTACT</a>
</li><li class="nav-item"><a class="nav-link link text-white display-7" href="page1.html">COURSE</a></li><li class="nav-item"><a class="nav-link link text-white display-7" href="https://ccumvllab.github.io/site/index.html">CHINESE VERSION</a></li></ul>
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<h1 class="mbr-section-title align-center mbr-bold pb-3 mbr-fonts-style display-2 text-title">National Chung Cheng University</h1>
<h3 class="mbr-section-subtitle align-center mbr-light pb-3 mbr-fonts-style display-1 text-title"><strong><em>Welcome To Machine Vision and Learning Lab</em></strong></h3>
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<section class="header1 cid-rkAFh7bGWK" id="header16-o">
<!--Slide Table-->
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<th class="col1 display-5">Date</th>
<th class="col2 display-5">News</th>
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<td class="col1 display-5">2019/5/22</td>
<td class="col2 display-5"><a href="http://scc.cilab.csie.ncu.edu.tw/work-ach.php?id=30248">Competition Work -- Let's tooth<br>won the third place in the 2018 national college software competition</a></td>
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<td class="col1 display-5">2019/5/22</td>
<td class="col2 display-5"><a href="http://scc.cilab.csie.ncu.edu.tw/work-ach.php?id=10133">Competition Work -- I'm superstar<br>won the third place in the 2016 national college software competition</a></td>
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<td class="col1 display-5">2019/5/22</td>
<td class="col2 display-5">Our MVL lab website is online</td>
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<h3 class="mbr-section-subtitle mbr-light pb-3 mbr-fonts-style display-2pm"><strong>RESEARCH</strong></h3>
<p class="mbr-text pb-3 mbr-fonts-style display-5">The research direction and results of our laboratory in recent years</p>
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<img src="assets/images/makeup-attack-628x279.png" alt="Mobirise" title="">
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<div class=" align-left aside-content">
<h2 class="mbr-title pt-2 mbr-fonts-style display-5"><strong>Incorporating attack information into makeup to attack deep learning models</strong></h2>
<div class="mbr-section-text">
<p class="mbr-text mb-5 pt-3 mbr-light mbr-fonts-style display-7">
Machine learning has evolved very rapidly, with good results in both computer vision and natural language processing. There are many deep learning techniques that are used in everyday life of humans such as autonomous vehicles and face recognition systems. Nowadays, the gradual dependence of human daily life on deep neural networks can lead to serious consequences, so the security of neural networks becomes very important. Therefore, the deep neural network has obvious weaknesses. We propose a method based on generating a confrontation network to generate a facial makeup picture that can deceive the face recognition system. We hide the perturbation of the attack in the results of the abnormal makeup photos that humans can’t detect. The experimental results show that we can not only generate high-quality facial makeup images, but also our attack results have a high attack success rate in the face recognition system.
</p>
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<img src="assets/images/music-game-628x396.png" alt="Mobirise" title="">
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<div class=" align-left aside-content">
<h2 class="mbr-title pt-2 mbr-fonts-style display-5"><strong>Using the Generative Adversarial Network(GAN) to generate music rhythm games</strong></h2>
<div class="mbr-section-text">
<p class="mbr-text mb-5 pt-3 mbr-light mbr-fonts-style display-7">
The music rhythm game is currently a very popular game, and we propose to generate a music rhythm game spectrum based on the method of Generative Adversarial Network. The music is separated into two parts: the vocal and the soundtrack, which makes the generated spectrum closer to the real spectrum. The model consists of two concepts of Generative Adversarial Network: Conditional Generative Adversarial Nets (CGANs) for music information and Improved Wasserstein GAN (WGAN-GP) for better convergence of the model.
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<img src="assets/images/scribble-lines-to-painting-520x237.jpg" alt="Mobirise" title="">
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<h2 class="mbr-title pt-2 mbr-fonts-style display-5"><strong>Be an Artist! Scribble Lines to Painting.</strong></h2>
<div class="mbr-section-text">
<p class="mbr-text mb-5 pt-3 mbr-light mbr-fonts-style display-7">
We propose a fully automated system that converts random graffiti into a painting. However, this is a serious challenge because the input graffiti can be very messy and hide multiple objects, so finding the correlation between these repeated lines and multiple objects is not a simple matter. In the system, we use selective search, sparse coding and Convolutional Neural Network (CNN), in which we use selective search to find the part of the object that may be the object of the graffiti; then use sparse coding to find the corresponding element; CNN sets the style to be converted. The final experimental results show that the methods we use have superior performance and produce artistic works.
</p>
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</section>
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<div class="container">
<div class="col-md-12">
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<div class="mbr-figure m-auto" style="width: 45%;">
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<h2 class="mbr-title pt-2 mbr-fonts-style display-5"><strong>Clothing style analysis and popular element capture</strong></h2>
<div class="mbr-section-text">
<p class="mbr-text mb-5 pt-3 mbr-light mbr-fonts-style display-7">With more and more styles of clothing and accessories, regardless of the physical or online store, consumers will spend a lot of time looking for their favorite styles in many styles, so if consumers can give some photos of their favorite costumes, systematic analysis Find out the relevant information in the photo (such as the store address, matching related accessories, etc.). For the store, if you can collect the relevant clothing styles of the customers, you can adjust the purchase styles and the furnishings in the store according to this information, further recommend the related accessories to consumers according to the preferences of consumers and save consumers to find matching accessories. time. For garment manufacturers, they can analyze the data collected by various stores to know which styles are popular and those styles are unpopular, and thus become the next batch of new style design references.<br></p>
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</section>
<section class="features11 cid-rkakVMFjTq" id="features11-s">
<div class="container">
<div class="col-md-12">
<div class="media-container-row">
<div class="mbr-figure m-auto" style="width: 45%;">
<img src="assets/images/60359706-2364327760514781-6025843136377389056-n-641x285.png" alt="Mobirise" title="">
</div>
<div class=" align-left aside-content">
<h2 class="mbr-title pt-2 mbr-fonts-style display-5"><strong>Deep Learning for Sensor-Based Rehabilitation
</strong><div><strong>Exercise Recognition and Evaluation</strong></div></h2>
<div class="mbr-section-text">
<p class="mbr-text mb-5 pt-3 mbr-light mbr-fonts-style display-7">In this work, we aimed to evaluate four kinds of rehabilitation exercises at three levels: good, average, and bad. We propose a novel evaluation method by learning the best feature of each class.
The idea was to design an evaluation matrix where each entry corresponded to one level of one exercise. By setting the largest number in one entry, the evaluation matrix could be used along with the output
<br>layer of the deep learning model to infer the best feature of that exercise at a particular level.
The evaluation score is obtained by examining the distance
<br>measure of the current feature and the best feature of that class.
We also collect a new rehabilitation
exercise dataset for the rehabilitation exercise evaluation. It contains four different rehabilitation
actions at three levels, defined by rehabilitation physicians.</p>
</div>
</div>
</div>
</div>
</div>
</section>
<section class="features11 cid-rkalMxthPP" id="features11-u">
<div class="container">
<div class="col-md-12">
<div class="media-container-row">
<div class="mbr-figure m-auto" style="width: 45%;">
<img src="assets/images/outdoor-low-resolution-face-recognition-554x261.png" alt="Mobirise" title="">
</div>
<div class=" align-left aside-content">
<h2 class="mbr-title pt-2 mbr-fonts-style display-5"><strong>Outdoor low resolution face recognition</strong></h2>
<div class="mbr-section-text">
<p class="mbr-text mb-5 pt-3 mbr-light mbr-fonts-style display-7">
The goal of this project is to compare low-resolution face images to verify that they are the same person. In today’s unrestricted environment, the effectiveness of face recognition often decreases due to posture factors, so we establish a normalization method to restore any face angle, thereby returning the face angle of any state to increase The effectiveness of face recognition. The project uses two Caffe model architectures: Matching-Convolutional NeuralNetwork (M-CNN) and Siamese Neural Network (SNN). Finally, the accuracy of the SNN model is more than 90%, which is higher than that of M-CNN.
</p>
</div>
</div>
</div>
</div>
</div>
</section>
<section class="features11 cid-rkam1Glnok" id="features11-v">
<div class="container">
<div class="col-md-12">
<div class="media-container-row">
<div class="mbr-figure m-auto" style="width: 45%;">
<img src="assets/images/image-multi-label-classification-628x461.jpg" alt="Mobirise" title="">
</div>
<div class=" align-left aside-content">
<h2 class="mbr-title pt-2 mbr-fonts-style display-5"><strong>Multiple attributes image classification</strong></h2>
<div class="mbr-section-text">
<p class="mbr-text mb-5 pt-3 mbr-light mbr-fonts-style display-7">
When sorting face images, there are inevitably some accessories in the images to be identified, such as sunglasses, scarves, earrings, etc., or external environmental factors such as light, angle, etc. These accessories or environmental factors are in people. The face image is called multiple attributes. We uses the existing Local Discriminant Embedding (LDE) algorithm as an extension to achieve multiple attribute classification purposes.
</p>
</div>
</div>
</div>
</div>
</div>
</section>
<section class="features11 cid-rkameIUc1w" id="features11-w">
<div class="container">
<div class="col-md-12">
<div class="media-container-row">
<div class="mbr-figure m-auto" style="width: 45%;">
<img src="assets/images/sparse-coding-628x463.jpg" alt="Mobirise" title="">
</div>
<div class=" align-left aside-content">
<h2 class="mbr-title pt-2 mbr-fonts-style display-5"><strong>Sparse Coding</strong></h2>
<div class="mbr-section-text">
<p class="mbr-text mb-5 pt-3 mbr-light mbr-fonts-style display-7">
In recent years, sparse coding has been very popular in the field of computer vision and image processing. Sparse coding consists of a linear combination of input data, dictionary and input data. Sparse coding can be used for image denoising, restoration, and classification. The laboratory focuses on two research directions based on sparse coding: multiple attribute image classification and sparse coding of huge amounts of data.
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<p class="mbr-text mbr-fonts-style display-4"><strong><font face="Noto Sans TC">
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<p class="mbr-text mbr-fonts-style display-4"><strong><font face="Noto Sans TC">陳彥名</font><br>Chen,Yan-Ming</strong><br></p>
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<small class="mbr-author-desc mbr-italic mbr-light mbr-fonts-style display-7"></small>
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<p class="mbr-text mbr-fonts-style display-4"><strong><font face="Noto Sans TC">江明修</font><br>Jiang, Ming-Xiu
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<p class="mbr-text mbr-fonts-style display-4"><strong><font face="Noto Sans TC">黃韋翔</font><br>Huang, Wei-Hsiang<br></strong></p>
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<div class="mbr-author-name mbr-bold mbr-fonts-style display-7">Master Student</div>
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<div class="card-block">
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<img src="assets/images/chein-hao-1-240x160.jpg" alt="" title="">
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<p class="mbr-text mbr-fonts-style display-4"><strong><font face="Noto Sans TC">
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張世亞</font><br>Chang, Shih-Ya<br></strong></p></div>
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張耘愷</font><br>Chang, Yun-Kai<br></strong></p></div>
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<img src="assets/images/min-syun.jpg" alt="" title="">
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<p class="mbr-text mbr-fonts-style display-4"><strong><font face="Noto Sans TC">
蔡旻勳</font><br>Cia, Min-Syun<br></strong></p></div>
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<div class="panel-item p-3">
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<img src="assets/images/hsuan-lun.jpg" alt="" title="">
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<p class="mbr-text mbr-fonts-style display-4"><strong><font face="Noto Sans TC">
江軒綸</font><br>Chiang, Hsuan-Lun<br></strong></p></div>
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<div class="mbr-author-name mbr-bold mbr-fonts-style display-7">Master Student</div>
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<div class="mbr-testimonial p-3 align-center col-12 col-md-6 col-lg-4">
<div class="panel-item p-3">
<div class="card-block">
<div class="testimonial-photo">
<img src="assets/images/yun-lun.jpg" alt="" title="">
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<p class="mbr-text mbr-fonts-style display-4"><strong><font face="Noto Sans TC">
李昀倫</font><br>Li, Yun-Lun<br></strong></p></div>
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<div class="mbr-author-name mbr-bold mbr-fonts-style display-7">Master Student</div>
<small class="mbr-author-desc mbr-italic mbr-light mbr-fonts-style display-7"></small>
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<img src="assets/images/yu-an.jpg" alt="" title="">
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<p class="mbr-text mbr-fonts-style display-4"><strong><font face="Noto Sans TC">
王佑安</font><br>Wang, Yu-An<br></strong></p></div>
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<p class="mbr-text mbr-fonts-style display-4"><strong><font face="Noto Sans TC">
蔡宜伶</font></strong><br><strong>Tsai, Yi-Ling</strong><br><strong>
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<p class="mbr-text mbr-fonts-style display-4"><strong><font face="Noto Sans TC">
林仕杰</font><br>Lin, Shi-Chieh</strong><br>
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<div class="mbr-testimonial p-3 align-center col-12 col-md-6 col-lg-4">
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<img src="assets/images/wangchenhan-240x240.jpg" alt="" title="">
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<p class="mbr-text mbr-fonts-style display-4"><strong><font face="Noto Sans TC">
王振翰</font><br>Wang, Chen-Han</strong></p>
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<p class="mbr-text mbr-fonts-style display-4"><strong><font face="Noto Sans TC">
盧允中</font></strong><br><strong>Lu, Yun-Chung</strong></p>
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宋東昱</font><br>Sung, Tung-Yu</strong></p>
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<div class="panel-item p-3">
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蔡佳君</font><br>Tsai, Chia-Chun
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<section class="testimonials1 cid-rqUeUDJXSJ" id="testimonials1-1i">
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<p class="mbr-text mbr-fonts-style display-4"><strong><font face="Noto Sans TC">
賴怡辰</font><br>Lai, Yi-Chen
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<small class="mbr-author-desc mbr-italic mbr-light mbr-fonts-style display-7"></small>
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<p class="mbr-text mbr-fonts-style display-4"><strong><font face="Noto Sans TC">
涂家維</font><br>Tu, Chia-Wei
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<small class="mbr-author-desc mbr-italic mbr-light mbr-fonts-style display-7"></small>
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<div class="mbr-testimonial p-3 align-center col-12 col-md-6 col-lg-4">
<div class="panel-item p-3">
<div class="card-block">
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<img src="assets/images/kangruielin-240x307.jpg" alt="" title="">
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<p class="mbr-text mbr-fonts-style display-4"><strong><font face="Noto Sans TC">
康瑞麟</font><br>Kang, Ruie-Lin
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<small class="mbr-author-desc mbr-italic mbr-light mbr-fonts-style display-7"></small>
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<div class="mbr-testimonial p-3 align-center col-12 col-md-6 col-lg-4">
<div class="panel-item p-3">
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<img src="assets/images/chenchengyeh-240x334.jpg" alt="" title="">
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<p class="mbr-text mbr-fonts-style display-4"><strong><font face="Noto Sans TC">
陳政曄</font><br>Chen, Cheng-Yeh
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<small class="mbr-author-desc mbr-italic mbr-light mbr-fonts-style display-7"></small>
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<div class="mbr-testimonial p-3 align-center col-12 col-md-6 col-lg-4">
<div class="panel-item p-3">
<div class="card-block">
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<img src="assets/images/youchihhsiang-240x323.jpg" alt="" title="">
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<p class="mbr-text mbr-fonts-style display-4"><strong><font face="Noto Sans TC">
游智翔</font><br>You, Chih-Hsiang
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<div class="mbr-author-name mbr-bold mbr-fonts-style display-7">Master Student</div>
<small class="mbr-author-desc mbr-italic mbr-light mbr-fonts-style display-7"></small>
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<div class="mbr-testimonial p-3 align-center col-12 col-md-6 col-lg-4">
<div class="panel-item p-3">
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<img src="assets/images/chenshihmin-240x320.jpg" alt="" title="">
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<p class="mbr-text mbr-fonts-style display-4"><strong><font face="Noto Sans TC">
陳士民</font><br>Chen, Shih-Min
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<small class="mbr-author-desc mbr-italic mbr-light mbr-fonts-style display-7"></small>
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</section>
<section class="testimonials1 cid-rqUeVprWTC" id="testimonials1-1j">
<div class="mbr-overlay" style="opacity: 0.5; background-color: rgb(239, 239, 239);">
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<div class="container">
<div class="media-container-row">
<div class="title col-12 align-center">
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<div class="container pt-3 mt-2">
<div class="media-container-row">
<div class="mbr-testimonial p-3 align-center col-12 col-md-6 col-lg-4">
<div class="panel-item p-3">
<div class="card-block">
<div class="testimonial-photo">
<img src="assets/images/wanghuihung-186x248.jpg" alt="" title="">
</div>
<p class="mbr-text mbr-fonts-style display-4"><strong><font face="Noto Sans TC">
王暉宏</font><br>Wang, Hui-Hung
</strong></p>
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<div class="card-footer">
<div class="mbr-author-name mbr-bold mbr-fonts-style display-7">Master Student</div>
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