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defense</title> <meta name="description" content="Search results for: adversarial defense"> <meta name="keywords" content="adversarial defense"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none 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action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="adversarial defense"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 542</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: adversarial defense</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">542</span> Literature Review: Adversarial Machine Learning Defense in Malware Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Leidy%20M.%20Aldana">Leidy M. Aldana</a>, <a href="https://publications.waset.org/abstracts/search?q=Jorge%20E.%20Camargo"> Jorge E. Camargo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Adversarial Machine Learning has gained importance in recent years as Cybersecurity has gained too, especially malware, it has affected different entities and people in recent years. This paper shows a literature review about defense methods created to prevent adversarial machine learning attacks, firstable it shows an introduction about the context and the description of some terms, in the results section some of the attacks are described, focusing on detecting adversarial examples before coming to the machine learning algorithm and showing other categories that exist in defense. A method with five steps is proposed in the method section in order to define a way to make the literature review; in addition, this paper summarizes the contributions in this research field in the last seven years to identify research directions in this area. About the findings, the category with least quantity of challenges in defense is the Detection of adversarial examples being this one a viable research route with the adaptive approach in attack and defense. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Malware" title="Malware">Malware</a>, <a href="https://publications.waset.org/abstracts/search?q=adversarial" title=" adversarial"> adversarial</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=defense" title=" defense"> defense</a>, <a href="https://publications.waset.org/abstracts/search?q=attack" title=" attack"> attack</a> </p> <a href="https://publications.waset.org/abstracts/177946/literature-review-adversarial-machine-learning-defense-in-malware-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/177946.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">63</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">541</span> Comprehensive Review of Adversarial Machine Learning in PDF Malware</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Preston%20Nabors">Preston Nabors</a>, <a href="https://publications.waset.org/abstracts/search?q=Nasseh%20Tabrizi"> Nasseh Tabrizi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Portable Document Format (PDF) files have gained significant popularity for sharing and distributing documents due to their universal compatibility. However, the widespread use of PDF files has made them attractive targets for cybercriminals, who exploit vulnerabilities to deliver malware and compromise the security of end-user systems. This paper reviews notable contributions in PDF malware detection, including static, dynamic, signature-based, and hybrid analysis. It presents a comprehensive examination of PDF malware detection techniques, focusing on the emerging threat of adversarial sampling and the need for robust defense mechanisms. The paper highlights the vulnerability of machine learning classifiers to evasion attacks. It explores adversarial sampling techniques in PDF malware detection to produce mimicry and reverse mimicry evasion attacks, which aim to bypass detection systems. Improvements for future research are identified, including accessible methods, applying adversarial sampling techniques to malicious payloads, evaluating other models, evaluating the importance of features to malware, implementing adversarial defense techniques, and conducting comprehensive examination across various scenarios. By addressing these opportunities, researchers can enhance PDF malware detection and develop more resilient defense mechanisms against adversarial attacks. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=adversarial%20attacks" title="adversarial attacks">adversarial attacks</a>, <a href="https://publications.waset.org/abstracts/search?q=adversarial%20defense" title=" adversarial defense"> adversarial defense</a>, <a href="https://publications.waset.org/abstracts/search?q=adversarial%20machine%20learning" title=" adversarial machine learning"> adversarial machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=intrusion%20detection" title=" intrusion detection"> intrusion detection</a>, <a href="https://publications.waset.org/abstracts/search?q=PDF%20malware" title=" PDF malware"> PDF malware</a>, <a href="https://publications.waset.org/abstracts/search?q=malware%20detection" title=" malware detection"> malware detection</a>, <a href="https://publications.waset.org/abstracts/search?q=malware%20detection%20evasion" title=" malware detection evasion"> malware detection evasion</a> </p> <a href="https://publications.waset.org/abstracts/184556/comprehensive-review-of-adversarial-machine-learning-in-pdf-malware" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/184556.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">39</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">540</span> Adversarial Attacks and Defenses on Deep Neural Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jonathan%20Sohn">Jonathan Sohn</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Deep neural networks (DNNs) have shown state-of-the-art performance for many applications, including computer vision, natural language processing, and speech recognition. Recently, adversarial attacks have been studied in the context of deep neural networks, which aim to alter the results of deep neural networks by modifying the inputs slightly. For example, an adversarial attack on a DNN used for object detection can cause the DNN to miss certain objects. As a result, the reliability of DNNs is undermined by their lack of robustness against adversarial attacks, raising concerns about their use in safety-critical applications such as autonomous driving. In this paper, we focus on studying the adversarial attacks and defenses on DNNs for image classification. There are two types of adversarial attacks studied which are fast gradient sign method (FGSM) attack and projected gradient descent (PGD) attack. A DNN forms decision boundaries that separate the input images into different categories. The adversarial attack slightly alters the image to move over the decision boundary, causing the DNN to misclassify the image. FGSM attack obtains the gradient with respect to the image and updates the image once based on the gradients to cross the decision boundary. PGD attack, instead of taking one big step, repeatedly modifies the input image with multiple small steps. There is also another type of attack called the target attack. This adversarial attack is designed to make the machine classify an image to a class chosen by the attacker. We can defend against adversarial attacks by incorporating adversarial examples in training. Specifically, instead of training the neural network with clean examples, we can explicitly let the neural network learn from the adversarial examples. In our experiments, the digit recognition accuracy on the MNIST dataset drops from 97.81% to 39.50% and 34.01% when the DNN is attacked by FGSM and PGD attacks, respectively. If we utilize FGSM training as a defense method, the classification accuracy greatly improves from 39.50% to 92.31% for FGSM attacks and from 34.01% to 75.63% for PGD attacks. To further improve the classification accuracy under adversarial attacks, we can also use a stronger PGD training method. PGD training improves the accuracy by 2.7% under FGSM attacks and 18.4% under PGD attacks over FGSM training. It is worth mentioning that both FGSM and PGD training do not affect the accuracy of clean images. In summary, we find that PGD attacks can greatly degrade the performance of DNNs, and PGD training is a very effective way to defend against such attacks. PGD attacks and defence are overall significantly more effective than FGSM methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=deep%20neural%20network" title="deep neural network">deep neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=adversarial%20attack" title=" adversarial attack"> adversarial attack</a>, <a href="https://publications.waset.org/abstracts/search?q=adversarial%20defense" title=" adversarial defense"> adversarial defense</a>, <a href="https://publications.waset.org/abstracts/search?q=adversarial%20machine%20learning" title=" adversarial machine learning"> adversarial machine learning</a> </p> <a href="https://publications.waset.org/abstracts/142005/adversarial-attacks-and-defenses-on-deep-neural-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/142005.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">195</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">539</span> Deep Reinforcement Learning and Generative Adversarial Networks Approach to Thwart Intrusions and Adversarial Attacks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fabrice%20Setephin%20Atedjio">Fabrice Setephin Atedjio</a>, <a href="https://publications.waset.org/abstracts/search?q=Jean-Pierre%20Lienou"> Jean-Pierre Lienou</a>, <a href="https://publications.waset.org/abstracts/search?q=Frederica%20F.%20Nelson"> Frederica F. Nelson</a>, <a href="https://publications.waset.org/abstracts/search?q=Sachin%20S.%20Shetty"> Sachin S. Shetty</a>, <a href="https://publications.waset.org/abstracts/search?q=Charles%20A.%20Kamhoua"> Charles A. Kamhoua</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Malicious users exploit vulnerabilities in computer systems, significantly disrupting their performance and revealing the inadequacies of existing protective solutions. Even machine learning-based approaches, designed to ensure reliability, can be compromised by adversarial attacks that undermine their robustness. This paper addresses two critical aspects of enhancing model reliability. First, we focus on improving model performance and robustness against adversarial threats. To achieve this, we propose a strategy by harnessing deep reinforcement learning. Second, we introduce an approach leveraging generative adversarial networks to counter adversarial attacks effectively. Our results demonstrate substantial improvements over previous works in the literature, with classifiers exhibiting enhanced accuracy in classification tasks, even in the presence of adversarial perturbations. These findings underscore the efficacy of the proposed model in mitigating intrusions and adversarial attacks within the machine-learning landscape. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title="machine learning">machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=reliability" title=" reliability"> reliability</a>, <a href="https://publications.waset.org/abstracts/search?q=adversarial%20attacks" title=" adversarial attacks"> adversarial attacks</a>, <a href="https://publications.waset.org/abstracts/search?q=deep-reinforcement%20learning" title=" deep-reinforcement learning"> deep-reinforcement learning</a>, <a href="https://publications.waset.org/abstracts/search?q=robustness" title=" robustness"> robustness</a> </p> <a href="https://publications.waset.org/abstracts/194008/deep-reinforcement-learning-and-generative-adversarial-networks-approach-to-thwart-intrusions-and-adversarial-attacks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/194008.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">9</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">538</span> Enhancement Method of Network Traffic Anomaly Detection Model Based on Adversarial Training With Category Tags</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zhang%20Shuqi">Zhang Shuqi</a>, <a href="https://publications.waset.org/abstracts/search?q=Liu%20Dan"> Liu Dan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> For the problems in intelligent network anomaly traffic detection models, such as low detection accuracy caused by the lack of training samples, poor effect with small sample attack detection, a classification model enhancement method, F-ACGAN(Flow Auxiliary Classifier Generative Adversarial Network) which introduces generative adversarial network and adversarial training, is proposed to solve these problems. Generating adversarial data with category labels could enhance the training effect and improve classification accuracy and model robustness. FACGAN consists of three steps: feature preprocess, which includes data type conversion, dimensionality reduction and normalization, etc.; A generative adversarial network model with feature learning ability is designed, and the sample generation effect of the model is improved through adversarial iterations between generator and discriminator. The adversarial disturbance factor of the gradient direction of the classification model is added to improve the diversity and antagonism of generated data and to promote the model to learn from adversarial classification features. The experiment of constructing a classification model with the UNSW-NB15 dataset shows that with the enhancement of FACGAN on the basic model, the classification accuracy has improved by 8.09%, and the score of F1 has improved by 6.94%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data%20imbalance" title="data imbalance">data imbalance</a>, <a href="https://publications.waset.org/abstracts/search?q=GAN" title=" GAN"> GAN</a>, <a href="https://publications.waset.org/abstracts/search?q=ACGAN" title=" ACGAN"> ACGAN</a>, <a href="https://publications.waset.org/abstracts/search?q=anomaly%20detection" title=" anomaly detection"> anomaly detection</a>, <a href="https://publications.waset.org/abstracts/search?q=adversarial%20training" title=" adversarial training"> adversarial training</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20augmentation" title=" data augmentation"> data augmentation</a> </p> <a href="https://publications.waset.org/abstracts/156929/enhancement-method-of-network-traffic-anomaly-detection-model-based-on-adversarial-training-with-category-tags" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/156929.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">105</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">537</span> Resisting Adversarial Assaults: A Model-Agnostic Autoencoder Solution</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Massimo%20Miccoli">Massimo Miccoli</a>, <a href="https://publications.waset.org/abstracts/search?q=Luca%20Marangoni"> Luca Marangoni</a>, <a href="https://publications.waset.org/abstracts/search?q=Alberto%20Aniello%20Scaringi"> Alberto Aniello Scaringi</a>, <a href="https://publications.waset.org/abstracts/search?q=Alessandro%20Marceddu"> Alessandro Marceddu</a>, <a href="https://publications.waset.org/abstracts/search?q=Alessandro%20Amicone"> Alessandro Amicone</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The susceptibility of deep neural networks (DNNs) to adversarial manipulations is a recognized challenge within the computer vision domain. Adversarial examples, crafted by adding subtle yet malicious alterations to benign images, exploit this vulnerability. Various defense strategies have been proposed to safeguard DNNs against such attacks, stemming from diverse research hypotheses. Building upon prior work, our approach involves the utilization of autoencoder models. Autoencoders, a type of neural network, are trained to learn representations of training data and reconstruct inputs from these representations, typically minimizing reconstruction errors like mean squared error (MSE). Our autoencoder was trained on a dataset of benign examples; learning features specific to them. Consequently, when presented with significantly perturbed adversarial examples, the autoencoder exhibited high reconstruction errors. The architecture of the autoencoder was tailored to the dimensions of the images under evaluation. We considered various image sizes, constructing models differently for 256x256 and 512x512 images. Moreover, the choice of the computer vision model is crucial, as most adversarial attacks are designed with specific AI structures in mind. To mitigate this, we proposed a method to replace image-specific dimensions with a structure independent of both dimensions and neural network models, thereby enhancing robustness. Our multi-modal autoencoder reconstructs the spectral representation of images across the red-green-blue (RGB) color channels. To validate our approach, we conducted experiments using diverse datasets and subjected them to adversarial attacks using models such as ResNet50 and ViT_L_16 from the torch vision library. The autoencoder extracted features used in a classification model, resulting in an MSE (RGB) of 0.014, a classification accuracy of 97.33%, and a precision of 99%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=adversarial%20attacks" title="adversarial attacks">adversarial attacks</a>, <a href="https://publications.waset.org/abstracts/search?q=malicious%20images%20detector" title=" malicious images detector"> malicious images detector</a>, <a href="https://publications.waset.org/abstracts/search?q=binary%20classifier" title=" binary classifier"> binary classifier</a>, <a href="https://publications.waset.org/abstracts/search?q=multimodal%20transformer%20autoencoder" title=" multimodal transformer autoencoder"> multimodal transformer autoencoder</a> </p> <a href="https://publications.waset.org/abstracts/174687/resisting-adversarial-assaults-a-model-agnostic-autoencoder-solution" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/174687.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">113</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">536</span> A Deep Reinforcement Learning-Based Secure Framework against Adversarial Attacks in Power System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Arshia%20Aflaki">Arshia Aflaki</a>, <a href="https://publications.waset.org/abstracts/search?q=Hadis%20Karimipour"> Hadis Karimipour</a>, <a href="https://publications.waset.org/abstracts/search?q=Anik%20Islam"> Anik Islam</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Generative Adversarial Attacks (GAAs) threaten critical sectors, ranging from fingerprint recognition to industrial control systems. Existing Deep Learning (DL) algorithms are not robust enough against this kind of cyber-attack. As one of the most critical industries in the world, the power grid is not an exception. In this study, a Deep Reinforcement Learning-based (DRL) framework assisting the DL model to improve the robustness of the model against generative adversarial attacks is proposed. Real-world smart grid stability data, as an IIoT dataset, test our method and improves the classification accuracy of a deep learning model from around 57 percent to 96 percent. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=generative%20adversarial%20attack" title="generative adversarial attack">generative adversarial attack</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20reinforcement%20learning" title=" deep reinforcement learning"> deep reinforcement learning</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=IIoT" title=" IIoT"> IIoT</a>, <a href="https://publications.waset.org/abstracts/search?q=generative%20adversarial%20networks" title=" generative adversarial networks"> generative adversarial networks</a>, <a href="https://publications.waset.org/abstracts/search?q=power%20system" title=" power system"> power system</a> </p> <a href="https://publications.waset.org/abstracts/188908/a-deep-reinforcement-learning-based-secure-framework-against-adversarial-attacks-in-power-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/188908.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">37</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">535</span> A Framework for Embedding Industry 4.0 in the UAE Defence Manufacturing Industry</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Khalifa%20Al%20Baloushi">Khalifa Al Baloushi</a>, <a href="https://publications.waset.org/abstracts/search?q=Hongwei%20Zhang"> Hongwei Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=Terrence%20Perera"> Terrence Perera</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Over the last few decades, the government of the UAE has been taking actions to consolidate defense manufacturing entities with the view to build a coherent and modern defense manufacturing base. Whilst these actions have significantly improved the overall capabilities of defense manufacturing; further opportunities exist to radically transform the sector. A comprehensive literature review and data collected from a survey identified three potential areas of improvements, (a) integration of Industry 4.0 technologies and other smart technologies, (b) stronger engagement of small and Medium-sized defense manufacturing companies and (c) Enhancing the national defense policies by embedding best practices from other nations. This research paper presents the design and development of a conceptual framework for the UAE defense industrial ecosystem. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=industry%204.0" title="industry 4.0">industry 4.0</a>, <a href="https://publications.waset.org/abstracts/search?q=defense%20manufacturing" title=" defense manufacturing"> defense manufacturing</a>, <a href="https://publications.waset.org/abstracts/search?q=eco-systems" title=" eco-systems"> eco-systems</a>, <a href="https://publications.waset.org/abstracts/search?q=integration" title=" integration"> integration</a> </p> <a href="https://publications.waset.org/abstracts/145808/a-framework-for-embedding-industry-40-in-the-uae-defence-manufacturing-industry" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/145808.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">207</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">534</span> Black-Box-Base Generic Perturbation Generation Method under Salient Graphs</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Dingyang%20Hu">Dingyang Hu</a>, <a href="https://publications.waset.org/abstracts/search?q=Dan%20Liu"> Dan Liu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> DNN (Deep Neural Network) deep learning models are widely used in classification, prediction, and other task scenarios. To address the difficulties of generic adversarial perturbation generation for deep learning models under black-box conditions, a generic adversarial ingestion generation method based on a saliency map (CJsp) is proposed to obtain salient image regions by counting the factors that influence the input features of an image on the output results. This method can be understood as a saliency map attack algorithm to obtain false classification results by reducing the weights of salient feature points. Experiments also demonstrate that this method can obtain a high success rate of migration attacks and is a batch adversarial sample generation method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=adversarial%20sample" title="adversarial sample">adversarial sample</a>, <a href="https://publications.waset.org/abstracts/search?q=gradient" title=" gradient"> gradient</a>, <a href="https://publications.waset.org/abstracts/search?q=probability" title=" probability"> probability</a>, <a href="https://publications.waset.org/abstracts/search?q=black%20box" title=" black box"> black box</a> </p> <a href="https://publications.waset.org/abstracts/156657/black-box-base-generic-perturbation-generation-method-under-salient-graphs" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/156657.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">104</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">533</span> Towards an Adversary-Aware ML-Based Detector of Spam on Twitter Hashtags</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Niddal%20Imam">Niddal Imam</a>, <a href="https://publications.waset.org/abstracts/search?q=Vassilios%20G.%20Vassilakis"> Vassilios G. Vassilakis</a> </p> <p class="card-text"><strong>Abstract:</strong></p> After analysing messages posted by health-related spam campaigns in Twitter Arabic hashtags, we found that these campaigns use unique hijacked accounts (we call them adversarial hijacked accounts) as adversarial examples to fool deployed ML-based spam detectors. Existing ML-based models build a behaviour profile for each user to detect hijacked accounts. This approach is not applicable for detecting spam in Twitter hashtags since they are computationally expensive. Hence, we propose an adversary-aware ML-based detector, which includes a newly designed feature (avg posts) to improve the detection of spam tweets posted by the adversarial hijacked accounts at a tweet-level in trending hashtags. The proposed detector was designed considering three key points: robustness, adaptability, and interpretability. The new feature leverages the account’s temporal patterns (i.e., account age and number of posts). It is faster to compute compared to features discussed in the literature and improves the accuracy of detecting the identified hijacked accounts by 73%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Twitter%20spam%20detection" title="Twitter spam detection">Twitter spam detection</a>, <a href="https://publications.waset.org/abstracts/search?q=adversarial%20examples" title=" adversarial examples"> adversarial examples</a>, <a href="https://publications.waset.org/abstracts/search?q=evasion%20attack" title=" evasion attack"> evasion attack</a>, <a href="https://publications.waset.org/abstracts/search?q=adversarial%20concept%20drift" title=" adversarial concept drift"> adversarial concept drift</a>, <a href="https://publications.waset.org/abstracts/search?q=account%20hijacking" title=" account hijacking"> account hijacking</a>, <a href="https://publications.waset.org/abstracts/search?q=trending%20hashtag" title=" trending hashtag"> trending hashtag</a> </p> <a href="https://publications.waset.org/abstracts/157771/towards-an-adversary-aware-ml-based-detector-of-spam-on-twitter-hashtags" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/157771.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">78</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">532</span> Non-Targeted Adversarial Image Classification Attack-Region Modification Methods</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bandar%20Alahmadi">Bandar Alahmadi</a>, <a href="https://publications.waset.org/abstracts/search?q=Lethia%20Jackson"> Lethia Jackson</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Machine Learning model is used today in many real-life applications. The safety and security of such model is important, so the results of the model are as accurate as possible. One challenge of machine learning model security is the adversarial examples attack. Adversarial examples are designed by the attacker to cause the machine learning model to misclassify the input. We propose a method to generate adversarial examples to attack image classifiers. We are modifying the successfully classified images, so a classifier misclassifies them after the modification. In our method, we do not update the whole image, but instead we detect the important region, modify it, place it back to the original image, and then run it through a classifier. The algorithm modifies the detected region using two methods. First, it will add abstract image matrix on back of the detected image matrix. Then, it will perform a rotation attack to rotate the detected region around its axes, and embed the trace of image in image background. Finally, the attacked region is placed in its original position, from where it was removed, and a smoothing filter is applied to smooth the background with foreground. We test our method in cascade classifier, and the algorithm is efficient, the classifier confident has dropped to almost zero. We also try it in CNN (Convolutional neural network) with higher setting and the algorithm was successfully worked. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=adversarial%20examples" title="adversarial examples">adversarial examples</a>, <a href="https://publications.waset.org/abstracts/search?q=attack" title=" attack"> attack</a>, <a href="https://publications.waset.org/abstracts/search?q=computer%20vision" title=" computer vision"> computer vision</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20processing" title=" image processing"> image processing</a> </p> <a href="https://publications.waset.org/abstracts/87510/non-targeted-adversarial-image-classification-attack-region-modification-methods" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/87510.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">339</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">531</span> Generative AI: A Comparison of Conditional Tabular Generative Adversarial Networks and Conditional Tabular Generative Adversarial Networks with Gaussian Copula in Generating Synthetic Data with Synthetic Data Vault</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lakshmi%20Prayaga">Lakshmi Prayaga</a>, <a href="https://publications.waset.org/abstracts/search?q=Chandra%20Prayaga.%20Aaron%20Wade"> Chandra Prayaga. Aaron Wade</a>, <a href="https://publications.waset.org/abstracts/search?q=Gopi%20Shankar%20Mallu"> Gopi Shankar Mallu</a>, <a href="https://publications.waset.org/abstracts/search?q=Harsha%20Satya%20Pola"> Harsha Satya Pola</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Synthetic data generated by Generative Adversarial Networks and Autoencoders is becoming more common to combat the problem of insufficient data for research purposes. However, generating synthetic data is a tedious task requiring extensive mathematical and programming background. Open-source platforms such as the Synthetic Data Vault (SDV) and Mostly AI have offered a platform that is user-friendly and accessible to non-technical professionals to generate synthetic data to augment existing data for further analysis. The SDV also provides for additions to the generic GAN, such as the Gaussian copula. We present the results from two synthetic data sets (CTGAN data and CTGAN with Gaussian Copula) generated by the SDV and report the findings. The results indicate that the ROC and AUC curves for the data generated by adding the layer of Gaussian copula are much higher than the data generated by the CTGAN. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=synthetic%20data%20generation" title="synthetic data generation">synthetic data generation</a>, <a href="https://publications.waset.org/abstracts/search?q=generative%20adversarial%20networks" title=" generative adversarial networks"> generative adversarial networks</a>, <a href="https://publications.waset.org/abstracts/search?q=conditional%20tabular%20GAN" title=" conditional tabular GAN"> conditional tabular GAN</a>, <a href="https://publications.waset.org/abstracts/search?q=Gaussian%20copula" title=" Gaussian copula"> Gaussian copula</a> </p> <a href="https://publications.waset.org/abstracts/183000/generative-ai-a-comparison-of-conditional-tabular-generative-adversarial-networks-and-conditional-tabular-generative-adversarial-networks-with-gaussian-copula-in-generating-synthetic-data-with-synthetic-data-vault" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/183000.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">82</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">530</span> Resilient Machine Learning in the Nuclear Industry: Crack Detection as a Case Study</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Anita%20Khadka">Anita Khadka</a>, <a href="https://publications.waset.org/abstracts/search?q=Gregory%20Epiphaniou"> Gregory Epiphaniou</a>, <a href="https://publications.waset.org/abstracts/search?q=Carsten%20Maple"> Carsten Maple</a> </p> <p class="card-text"><strong>Abstract:</strong></p> There is a dramatic surge in the adoption of machine learning (ML) techniques in many areas, including the nuclear industry (such as fault diagnosis and fuel management in nuclear power plants), autonomous systems (including self-driving vehicles), space systems (space debris recovery, for example), medical surgery, network intrusion detection, malware detection, to name a few. With the application of learning methods in such diverse domains, artificial intelligence (AI) has become a part of everyday modern human life. To date, the predominant focus has been on developing underpinning ML algorithms that can improve accuracy, while factors such as resiliency and robustness of algorithms have been largely overlooked. If an adversarial attack is able to compromise the learning method or data, the consequences can be fatal, especially but not exclusively in safety-critical applications. In this paper, we present an in-depth analysis of five adversarial attacks and three defence methods on a crack detection ML model. Our analysis shows that it can be dangerous to adopt machine learning techniques in security-critical areas such as the nuclear industry without rigorous testing since they may be vulnerable to adversarial attacks. While common defence methods can effectively defend against different attacks, none of the three considered can provide protection against all five adversarial attacks analysed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=adversarial%20machine%20learning" title="adversarial machine learning">adversarial machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=attacks" title=" attacks"> attacks</a>, <a href="https://publications.waset.org/abstracts/search?q=defences" title=" defences"> defences</a>, <a href="https://publications.waset.org/abstracts/search?q=nuclear%20industry" title=" nuclear industry"> nuclear industry</a>, <a href="https://publications.waset.org/abstracts/search?q=crack%20detection" title=" crack detection"> crack detection</a> </p> <a href="https://publications.waset.org/abstracts/147458/resilient-machine-learning-in-the-nuclear-industry-crack-detection-as-a-case-study" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/147458.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">158</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">529</span> Adversarial Disentanglement Using Latent Classifier for Pose-Independent Representation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hamed%20Alqahtani">Hamed Alqahtani</a>, <a href="https://publications.waset.org/abstracts/search?q=Manolya%20Kavakli-Thorne"> Manolya Kavakli-Thorne</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The large pose discrepancy is one of the critical challenges in face recognition during video surveillance. Due to the entanglement of pose attributes with identity information, the conventional approaches for pose-independent representation lack in providing quality results in recognizing largely posed faces. In this paper, we propose a practical approach to disentangle the pose attribute from the identity information followed by synthesis of a face using a classifier network in latent space. The proposed approach employs a modified generative adversarial network framework consisting of an encoder-decoder structure embedded with a classifier in manifold space for carrying out factorization on the latent encoding. It can be further generalized to other face and non-face attributes for real-life video frames containing faces with significant attribute variations. Experimental results and comparison with state of the art in the field prove that the learned representation of the proposed approach synthesizes more compelling perceptual images through a combination of adversarial and classification losses. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=disentanglement" title="disentanglement">disentanglement</a>, <a href="https://publications.waset.org/abstracts/search?q=face%20detection" title=" face detection"> face detection</a>, <a href="https://publications.waset.org/abstracts/search?q=generative%20adversarial%20networks" title=" generative adversarial networks"> generative adversarial networks</a>, <a href="https://publications.waset.org/abstracts/search?q=video%20surveillance" title=" video surveillance"> video surveillance</a> </p> <a href="https://publications.waset.org/abstracts/108319/adversarial-disentanglement-using-latent-classifier-for-pose-independent-representation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/108319.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">129</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">528</span> MULTI-FLGANs: Multi-Distributed Adversarial Networks for Non-Independent and Identically Distributed Distribution</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Akash%20Amalan">Akash Amalan</a>, <a href="https://publications.waset.org/abstracts/search?q=Rui%20Wang"> Rui Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Yanqi%20Qiao"> Yanqi Qiao</a>, <a href="https://publications.waset.org/abstracts/search?q=Emmanouil%20Panaousis"> Emmanouil Panaousis</a>, <a href="https://publications.waset.org/abstracts/search?q=Kaitai%20Liang"> Kaitai Liang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Federated learning is an emerging concept in the domain of distributed machine learning. This concept has enabled General Adversarial Networks (GANs) to benefit from the rich distributed training data while preserving privacy. However, in a non-IID setting, current federated GAN architectures are unstable, struggling to learn the distinct features, and vulnerable to mode collapse. In this paper, we propose an architecture MULTI-FLGAN to solve the problem of low-quality images, mode collapse, and instability for non-IID datasets. Our results show that MULTI-FLGAN is four times as stable and performant (i.e., high inception score) on average over 20 clients compared to baseline FLGAN. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=federated%20learning" title="federated learning">federated learning</a>, <a href="https://publications.waset.org/abstracts/search?q=generative%20adversarial%20network" title=" generative adversarial network"> generative adversarial network</a>, <a href="https://publications.waset.org/abstracts/search?q=inference%20attack" title=" inference attack"> inference attack</a>, <a href="https://publications.waset.org/abstracts/search?q=non-IID%20data%20distribution" title=" non-IID data distribution"> non-IID data distribution</a> </p> <a href="https://publications.waset.org/abstracts/152877/multi-flgans-multi-distributed-adversarial-networks-for-non-independent-and-identically-distributed-distribution" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/152877.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">158</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">527</span> Performance Based Logistics and Applications in Turkey</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ferhat%20Yilmaz">Ferhat Yilmaz</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Defense sector is one of the most important areas where logistics is used extensively. Nations give importance to their defense spending in order to survive in their geography. Parallel to the rising crises around the world, governments increase their defense spending; however, resources are limited while the needs are infinite. Therefore, countries try to develop a more effective use of their defense budget. In order to make logistics more effective and efficient, performance- based logistical system was developed. This article tries to explain the Performance-based Logistical System, its employment process, employment areas, and how it will be used along with other main systems in the Turkey. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=performance" title="performance">performance</a>, <a href="https://publications.waset.org/abstracts/search?q=performance%20based%20logistics%20applications" title=" performance based logistics applications"> performance based logistics applications</a>, <a href="https://publications.waset.org/abstracts/search?q=logistical%20system" title=" logistical system"> logistical system</a>, <a href="https://publications.waset.org/abstracts/search?q=Turkey" title=" Turkey"> Turkey</a> </p> <a href="https://publications.waset.org/abstracts/31834/performance-based-logistics-and-applications-in-turkey" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31834.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">482</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">526</span> Self-denigration in Doctoral Defense Sessions: Scale Development and Validation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Alireza%20Jalilifar">Alireza Jalilifar</a>, <a href="https://publications.waset.org/abstracts/search?q=Nadia%20Mayahi"> Nadia Mayahi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The dissertation defense as a complicated conflict-prone context entails the adoption of elegant interactional strategies, one of which is self-denigration. This study aimed to develop and validate a self-denigration model that fits the context of doctoral defense sessions in applied linguistics. Two focus group discussions provided the basis for developing this conceptual model, which assumed 10 functions for self-denigration, namely good manners, modesty, affability, altruism, assertiveness, diffidence, coercive self-deprecation, evasion, diplomacy, and flamboyance. These functions were used to design a 40-item questionnaire on the attitudes of applied linguists concerning self-denigration in defense sessions. The confirmatory factor analysis of the questionnaire indicated the predictive ability of the measurement model. The findings of this study suggest that self-denigration in doctoral defense sessions is the social representation of the participants’ values, ideas and practices adopted as a negotiation strategy and a conflict management policy for the purpose of establishing harmony and maintaining resilience. This study has implications for doctoral students and academics and illuminates further research on self-denigration in other contexts. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=academic%20discourse" title="academic discourse">academic discourse</a>, <a href="https://publications.waset.org/abstracts/search?q=politeness" title=" politeness"> politeness</a>, <a href="https://publications.waset.org/abstracts/search?q=self-denigration" title=" self-denigration"> self-denigration</a>, <a href="https://publications.waset.org/abstracts/search?q=grounded%20theory" title=" grounded theory"> grounded theory</a>, <a href="https://publications.waset.org/abstracts/search?q=dissertation%20defense" title=" dissertation defense"> dissertation defense</a> </p> <a href="https://publications.waset.org/abstracts/143887/self-denigration-in-doctoral-defense-sessions-scale-development-and-validation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/143887.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">137</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">525</span> Defense Mechanism Maturity and the Severity of Mood Disorder Symptoms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Maja%20Pand%C5%BEa">Maja Pandža</a>, <a href="https://publications.waset.org/abstracts/search?q=Sanjin%20Lovri%C4%87"> Sanjin Lovrić</a>, <a href="https://publications.waset.org/abstracts/search?q=Iva%20%C4%8Colak"> Iva Čolak</a>, <a href="https://publications.waset.org/abstracts/search?q=Josipa%20Mandari%C4%87"> Josipa Mandarić</a>, <a href="https://publications.waset.org/abstracts/search?q=Miro%20Klari%C4%87"> Miro Klarić</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study explores the role of symptoms related to mood disorders salience on different types of defense mechanisms (mature, neurotic, immature) predominance. Total of 177 both clinical and non-clinical participants in Mostar, Bosnia & Herzegovina, completed a battery of questionnaires associated with defense mechanisms and self-reported depression and anxiety symptoms. The sample was additionally divided into four groups, given the level of symptoms experienced: 1. minimal, 2. mild, 3. moderate, 4. severe depression/anxiety. Participants with minimal anxiety and depression symptoms use mature defense mechanisms more often than other three groups. Immature mechanisms are most commonly used by the group with severe depression/anxiety levels in comparison with other groups. These differences are discussed on the dynamic level of analysis to have a better understanding of the relationship between defense mechanisms' maturity and degree of mood disorders' symptom severity. Also, results given could serve as an implication for the psychotherapeutic treatment plans. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=anxiety%2Fdepression%20symptoms" title="anxiety/depression symptoms">anxiety/depression symptoms</a>, <a href="https://publications.waset.org/abstracts/search?q=clinical%2Fnon-clinical%20sample" title=" clinical/non-clinical sample"> clinical/non-clinical sample</a>, <a href="https://publications.waset.org/abstracts/search?q=defense%20mechanism%20maturity" title=" defense mechanism maturity"> defense mechanism maturity</a>, <a href="https://publications.waset.org/abstracts/search?q=dynamic%20approach" title=" dynamic approach"> dynamic approach</a> </p> <a href="https://publications.waset.org/abstracts/34314/defense-mechanism-maturity-and-the-severity-of-mood-disorder-symptoms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/34314.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">457</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">524</span> A Macroeconomic Analysis of Defense Industry: Comparisons, Trends and Improvements in Brazil and in the World</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=J.%20Fajardo">J. Fajardo</a>, <a href="https://publications.waset.org/abstracts/search?q=J.%20Guerra"> J. Guerra</a>, <a href="https://publications.waset.org/abstracts/search?q=E.%20Gonzales"> E. Gonzales</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper will outline a study of Brazil's industrial base of defense (IDB), through a bibliographic research method, combined with an analysis of macroeconomic data from several available public data platforms. This paper begins with a brief study about Brazilian national industry, including analyzes of productivity, income, outcome and jobs. Next, the research presents a study on the defense industry in Brazil, presenting the main national companies that operate in the aeronautical, army and naval branches. After knowing the main points of the Brazilian defense industry, data on the productivity of the defense industry of the main countries and competing companies of the Brazilian industry were analyzed, in order to summarize big cases in Brazil with a comparative analysis. Concerned the methodology, were used bibliographic research and the exploration of historical data series, in order to analyze information, to get trends and to make comparisons along the time. The research is finished with the main trends for the development of the Brazilian defense industry, comparing the current situation with the point of view of several countries. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=economics%20of%20defence" title="economics of defence">economics of defence</a>, <a href="https://publications.waset.org/abstracts/search?q=industry" title=" industry"> industry</a>, <a href="https://publications.waset.org/abstracts/search?q=trends" title=" trends"> trends</a>, <a href="https://publications.waset.org/abstracts/search?q=market" title=" market"> market</a> </p> <a href="https://publications.waset.org/abstracts/99239/a-macroeconomic-analysis-of-defense-industry-comparisons-trends-and-improvements-in-brazil-and-in-the-world" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/99239.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">155</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">523</span> Non-Targeted Adversarial Object Detection Attack: Fast Gradient Sign Method</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bandar%20Alahmadi">Bandar Alahmadi</a>, <a href="https://publications.waset.org/abstracts/search?q=Manohar%20Mareboyana"> Manohar Mareboyana</a>, <a href="https://publications.waset.org/abstracts/search?q=Lethia%20Jackson"> Lethia Jackson</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Today, there are many applications that are using computer vision models, such as face recognition, image classification, and object detection. The accuracy of these models is very important for the performance of these applications. One challenge that facing the computer vision models is the adversarial examples attack. In computer vision, the adversarial example is an image that is intentionally designed to cause the machine learning model to misclassify it. One of very well-known method that is used to attack the Convolution Neural Network (CNN) is Fast Gradient Sign Method (FGSM). The goal of this method is to find the perturbation that can fool the CNN using the gradient of the cost function of CNN. In this paper, we introduce a novel model that can attack Regional-Convolution Neural Network (R-CNN) that use FGSM. We first extract the regions that are detected by R-CNN, and then we resize these regions into the size of regular images. Then, we find the best perturbation of the regions that can fool CNN using FGSM. Next, we add the resulted perturbation to the attacked region to get a new region image that looks similar to the original image to human eyes. Finally, we placed the regions back to the original image and test the R-CNN with the attacked images. Our model could drop the accuracy of the R-CNN when we tested with Pascal VOC 2012 dataset. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=adversarial%20examples" title="adversarial examples">adversarial examples</a>, <a href="https://publications.waset.org/abstracts/search?q=attack" title=" attack"> attack</a>, <a href="https://publications.waset.org/abstracts/search?q=computer%20vision" title=" computer vision"> computer vision</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20processing" title=" image processing"> image processing</a> </p> <a href="https://publications.waset.org/abstracts/103308/non-targeted-adversarial-object-detection-attack-fast-gradient-sign-method" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/103308.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">193</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">522</span> Implementation of Gender Policy in the Georgian National Defence: Key Issues and Challenges</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Vephkhvia%20Grigalashvili">Vephkhvia Grigalashvili</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The defense of Georgia is every citizen’s duty. The present article reviews the principles and standards of gender policy in the Georgian national defense sector. In addition, it looks at mechanisms for ensuring gender equality, going through the relevant Georgian legislation. Furthermore, this work aims to conduct a comparative analysis of defense models of Georgia, Finland, and the Baltic States in order to identify core institutional challenges. The study produced the following findings:(a) The national defense planning is based on the Total Defense approach, which implies a wide involvement of the country`s population in state defense. (b) This political act does not specify gender equality aspects of the Total Defense strategy; (c) According to the Constitution of Georgia, irrespective of gender factors, every citizen of Georgia is legally obliged to participate in state security activities. However, the state has an authority (power of choice) to decide which gender group (male or/and female citizen) must fulfill above mentioned their constitutional commitment. For instance, completion of compulsory military and reserve military services is a male citizen’s duty, whereas professional military service is equally accessible to both genders. The study concludes that effective implementation of the Total Defense concept largely depends on how Georgia uses its capabilities and human resources. Based on the statistical fact that more than 50% of the country’s population are women, Georgia has to elaborate on relevant institutional mechanisms for implementation of gender equality in the national defense organization. In this regard, it would be advisable: (i) to give the legal opportunity to women to serve in compulsory military service, and (ii) to develop labor reserve service as a part of the anti-crisis management system of Georgia. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=gender%20in%20defense%20organisation" title="gender in defense organisation">gender in defense organisation</a>, <a href="https://publications.waset.org/abstracts/search?q=gender%20mechanisms" title=" gender mechanisms"> gender mechanisms</a>, <a href="https://publications.waset.org/abstracts/search?q=gender%20in%20defense%20policy" title=" gender in defense policy"> gender in defense policy</a>, <a href="https://publications.waset.org/abstracts/search?q=gender%20policy" title=" gender policy"> gender policy</a> </p> <a href="https://publications.waset.org/abstracts/111949/implementation-of-gender-policy-in-the-georgian-national-defence-key-issues-and-challenges" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/111949.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">161</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">521</span> The Limits to Self-Defense Claims in Case of Domestic Violence Homicides</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Maria%20Elisabete%20Costa%20Ferreira">Maria Elisabete Costa Ferreira</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Domestic violence is a serious social issue in which victims are mostly women. Domestic violence develops in cycles, starting with the building of tension, passing through the incident of abuse and ending with reconciliation, also known as honeymoon. As time goes by, the shorter these phases become, and the greater and more severe the attacks, rarely leading to the death of the victim of abuse. Sometimes, the victim stops the abuse by killing the aggressor, usually after the immediate aggression has taken place. This poses an important obstacle to the claim of self-defense by the victim of domestic violence pending trial for the homicide of her long-time abuser. The main problem with self-defense claims in such cases is that the law requires the act of aggression to be present or imminent (imminent threat or immediate danger) so that it permits the victim to take her defense into her own hands. If the episode of aggression has already taken place, this general requirement for the admissibility of self-defense is not satisfied. This paper sheds new light on the concept of the actuality of the aggression, understanding that, since domestic violence is a permanent offense, for as long as the victim stays under the domain of the aggressor, imminent threat will be present, allowing the self-defense claim of a woman who kills her abuser in such circumstances to be admissible. An actualist interpretation of the requirement of the necessity of the means used in self-defense will be satisfied when evaluated from the subjective perspective of the intimate partner victim. Necessity will be satisfied if it is reasonable for the victim to perceive the use of lethal force as the only means to release herself from the abuser. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=domestic%20violence" title="domestic violence">domestic violence</a>, <a href="https://publications.waset.org/abstracts/search?q=homicide" title=" homicide"> homicide</a>, <a href="https://publications.waset.org/abstracts/search?q=self-defense" title=" self-defense"> self-defense</a>, <a href="https://publications.waset.org/abstracts/search?q=imminent%20threat" title=" imminent threat"> imminent threat</a>, <a href="https://publications.waset.org/abstracts/search?q=necessity%20of%20lethal%20force" title=" necessity of lethal force"> necessity of lethal force</a> </p> <a href="https://publications.waset.org/abstracts/171695/the-limits-to-self-defense-claims-in-case-of-domestic-violence-homicides" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/171695.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">65</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">520</span> Predicting the Relationship Between Childhood Trauma on the Formation of Defense Mechanisms with the Mediating Role of Object Relations in Traders</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ahmadreza%20Jabalameli">Ahmadreza Jabalameli</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Ebrahimpour%20Borujeni"> Mohammad Ebrahimpour Borujeni</a> </p> <p class="card-text"><strong>Abstract:</strong></p> According to psychodynamic theories, the major personality structure of individuals is formed in the first years of life. Trauma is an inseparable and undeniable part of everyone's life and they inevitably struggle with many traumas that can have a very significant impact on their lives. The present study deals with the relationship between childhood trauma on the formation of defense mechanisms and the role of object relations. The present descriptive study is a correlation with structural equation modeling (SEM). Sample selection is available and consists of 200 knowledgeable traders in Jabalameli Information Technology Company. The results indicate that the experience of childhood trauma with a demographic moderating effect, through the mediating role of object relations can lead to vulnerability to ego reality functionality and immature and psychically disturbed defense mechanisms. In this regard, there is a significant negative relationship between childhood trauma and object relations with mature defense mechanisms. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=childhood%20trauma" title="childhood trauma">childhood trauma</a>, <a href="https://publications.waset.org/abstracts/search?q=defense%20mechanisms" title=" defense mechanisms"> defense mechanisms</a>, <a href="https://publications.waset.org/abstracts/search?q=object%20relations" title=" object relations"> object relations</a>, <a href="https://publications.waset.org/abstracts/search?q=trade" title=" trade"> trade</a> </p> <a href="https://publications.waset.org/abstracts/148276/predicting-the-relationship-between-childhood-trauma-on-the-formation-of-defense-mechanisms-with-the-mediating-role-of-object-relations-in-traders" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/148276.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">132</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">519</span> The Liberal Tension of the Adversarial Criminal ‎Procedure</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Benjamin%20Newman">Benjamin Newman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The picture of an adverse contest between two parties has often been used as an archetypal description of the Anglo-American adversarial criminal trial. However, in actuality, guilty pleas and plea-bargains have been dominating the procedure for over the last half-a-century. Characterised by two adverse parties, the court adjudicative system in the Anglo-American world adhere to the adversarial procedure, and while further features have been attributed and the values that are embedded within the procedure vary, it is a system that we have no adequate theory. Damaska had argued that the adversarial conflict-resolution mode of administration of justice stems from a liberal laissez-faire concept of a value neutral liberal state. Having said that, the court’s neutrality has been additionally rationalised in light of its liberal end as a safeguard from the state’s coercive force. Both conceptions of the court’s neutrality conflict in cases where the by-standing role disposes of its liberal duty in safeguarding the individual. Such is noticeable in plea bargains, where the defendant has the liberty to plead guilty, despite concerns over wrongful convictions and deprivation of liberty. It is an inner liberal tension within the notion of criminal adversarialism, between the laissez-faire mode which grants autonomy to the parties and the safeguarding liberal end of the trial. Langbein had asserted that the adversarial system is a criminal procedure for which we have no adequate theory, and it is by reference to political and moral theories that the research aims to articulate a normative account. The paper contemplates on the above liberal-tension, and by reference to Duff’s ‘calling-to-account’ theory, argues that autonomy is of inherent value to the criminal process, being considered a constitutive element in the process of being called to account. While the aspiration is that the defendant’s guilty plea should be genuine, the guilty-plea decision must be voluntary if it is to be considered a performative act of accountability. Thus, by valuing procedural autonomy as a necessary element within the criminal adjudicative process, it assimilates a liberal procedure, whilst maintaining the liberal end by holding the defendant to account. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=liberal%20theory" title="liberal theory">liberal theory</a>, <a href="https://publications.waset.org/abstracts/search?q=adversarial%20criminal%20procedure" title=" adversarial criminal procedure"> adversarial criminal procedure</a>, <a href="https://publications.waset.org/abstracts/search?q=criminal%20law%20theory" title=" criminal law theory"> criminal law theory</a>, <a href="https://publications.waset.org/abstracts/search?q=liberal%20perfectionism" title=" liberal perfectionism"> liberal perfectionism</a>, <a href="https://publications.waset.org/abstracts/search?q=political%20liberalism" title=" political liberalism"> political liberalism</a> </p> <a href="https://publications.waset.org/abstracts/156686/the-liberal-tension-of-the-adversarial-criminal-procedure" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/156686.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">92</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">518</span> Enhancing the Effectiveness of Air Defense Systems through Simulation Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=F.%20Felipe">F. Felipe</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Air Defense Systems contain high-value assets that are expected to fulfill their mission for several years - in many cases, even decades - while operating in a fast-changing, technology-driven environment. Thus, it is paramount that decision-makers can assess how effective an Air Defense System is in the face of new developing threats, as well as to identify the bottlenecks that could jeopardize the security of the airspace of a country. Given the broad extent of activities and the great variety of assets necessary to achieve the strategic objectives, a systems approach was taken in order to delineate the core requirements and the physical architecture of an Air Defense System. Then, value-focused thinking helped in the definition of the measures of effectiveness. Furthermore, analytical methods were applied to create a formal structure that preliminarily assesses such measures. To validate the proposed methodology, a powerful simulation was also used to determine the measures of effectiveness, now in more complex environments that incorporate both uncertainty and multiple interactions of the entities. The results regarding the validity of this methodology suggest that the approach can support decisions aimed at enhancing the capabilities of Air Defense Systems. In conclusion, this paper sheds some light on how consolidated approaches of Systems Engineering and Operations Research can be used as valid techniques for solving problems regarding a complex and yet vital matter. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=air%20defense" title="air defense">air defense</a>, <a href="https://publications.waset.org/abstracts/search?q=effectiveness" title=" effectiveness"> effectiveness</a>, <a href="https://publications.waset.org/abstracts/search?q=system" title=" system"> system</a>, <a href="https://publications.waset.org/abstracts/search?q=simulation" title=" simulation"> simulation</a>, <a href="https://publications.waset.org/abstracts/search?q=decision-support" title=" decision-support"> decision-support</a> </p> <a href="https://publications.waset.org/abstracts/125149/enhancing-the-effectiveness-of-air-defense-systems-through-simulation-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/125149.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">156</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">517</span> Deep Feature Augmentation with Generative Adversarial Networks for Class Imbalance Learning in Medical Images</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rongbo%20Shen">Rongbo Shen</a>, <a href="https://publications.waset.org/abstracts/search?q=Jianhua%20Yao"> Jianhua Yao</a>, <a href="https://publications.waset.org/abstracts/search?q=Kezhou%20Yan"> Kezhou Yan</a>, <a href="https://publications.waset.org/abstracts/search?q=Kuan%20Tian"> Kuan Tian</a>, <a href="https://publications.waset.org/abstracts/search?q=Cheng%20Jiang"> Cheng Jiang</a>, <a href="https://publications.waset.org/abstracts/search?q=Ke%20Zhou"> Ke Zhou</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study proposes a generative adversarial networks (GAN) framework to perform synthetic sampling in feature space, i.e., feature augmentation, to address the class imbalance problem in medical image analysis. A feature extraction network is first trained to convert images into feature space. Then the GAN framework incorporates adversarial learning to train a feature generator for the minority class through playing a minimax game with a discriminator. The feature generator then generates features for minority class from arbitrary latent distributions to balance the data between the majority class and the minority class. Additionally, a data cleaning technique, i.e., Tomek link, is employed to clean up undesirable conflicting features introduced from the feature augmentation and thus establish well-defined class clusters for the training. The experiment section evaluates the proposed method on two medical image analysis tasks, i.e., mass classification on mammogram and cancer metastasis classification on histopathological images. Experimental results suggest that the proposed method obtains superior or comparable performance over the state-of-the-art counterparts. Compared to all counterparts, our proposed method improves more than 1.5 percentage of accuracy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=class%20imbalance" title="class imbalance">class imbalance</a>, <a href="https://publications.waset.org/abstracts/search?q=synthetic%20sampling" title=" synthetic sampling"> synthetic sampling</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20augmentation" title=" feature augmentation"> feature augmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=generative%20adversarial%20networks" title=" generative adversarial networks"> generative adversarial networks</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20cleaning" title=" data cleaning"> data cleaning</a> </p> <a href="https://publications.waset.org/abstracts/114272/deep-feature-augmentation-with-generative-adversarial-networks-for-class-imbalance-learning-in-medical-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/114272.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">127</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">516</span> A Generative Adversarial Framework for Bounding Confounded Causal Effects</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yaowei%20Hu">Yaowei Hu</a>, <a href="https://publications.waset.org/abstracts/search?q=Yongkai%20Wu"> Yongkai Wu</a>, <a href="https://publications.waset.org/abstracts/search?q=Lu%20Zhang"> Lu Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=Xintao%20Wu"> Xintao Wu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Causal inference from observational data is receiving wide applications in many fields. However, unidentifiable situations, where causal effects cannot be uniquely computed from observational data, pose critical barriers to applying causal inference to complicated real applications. In this paper, we develop a bounding method for estimating the average causal effect (ACE) under unidentifiable situations due to hidden confounders. We propose to parameterize the unknown exogenous random variables and structural equations of a causal model using neural networks and implicit generative models. Then, with an adversarial learning framework, we search the parameter space to explicitly traverse causal models that agree with the given observational distribution and find those that minimize or maximize the ACE to obtain its lower and upper bounds. The proposed method does not make any assumption about the data generating process and the type of the variables. Experiments using both synthetic and real-world datasets show the effectiveness of the method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=average%20causal%20effect" title="average causal effect">average causal effect</a>, <a href="https://publications.waset.org/abstracts/search?q=hidden%20confounding" title=" hidden confounding"> hidden confounding</a>, <a href="https://publications.waset.org/abstracts/search?q=bound%20estimation" title=" bound estimation"> bound estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=generative%20adversarial%20learning" title=" generative adversarial learning"> generative adversarial learning</a> </p> <a href="https://publications.waset.org/abstracts/127808/a-generative-adversarial-framework-for-bounding-confounded-causal-effects" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/127808.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">191</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">515</span> The Role of Defense Mechanisms in Treatment Adherence in Type 2 Diabetes Mellitus: An Exploratory Study</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=F.%20Marchini">F. Marchini</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Caputo"> A. Caputo</a>, <a href="https://publications.waset.org/abstracts/search?q=J.%20Balonan"> J. Balonan</a>, <a href="https://publications.waset.org/abstracts/search?q=F.%20Fedele"> F. Fedele</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Napoli"> A. Napoli</a>, <a href="https://publications.waset.org/abstracts/search?q=V.%20Langher"> V. Langher</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Aim: The present study aims to explore the specific role of defense mechanisms in persons with type 2 diabetes mellitus in treatment adherence. Materials and methods: A correlational study design was employed. Thirty-two persons with type 2 diabetes mellitus were enrolled and assessed with Defense Mechanism Inventory, Beck Depression Inventory-II, Toronto Alexithymia Scale and Self-Care Inventory-Revised. Bivariate correlation and two-step regression analyses were performed. Results: Treatment adherence negatively correlates with hetero-directed hostility (r= -.537; p < .01), whereas it is positively associated with principalization (r= .407; p < .05). These two defense mechanisms overall explain an incremental variance of 26.9% in treatment adherence (ΔF=4.189, df1=2, df2 =21, p < .05), over and above the control variables for depression and alexithymia. However, only higher hetero-directed hostility is found to be a solid predictor of a decreased treatment adherence (β=-.497, p < .05). Conclusions: Despite providing preliminary results, this pilot study highlights the original contribution of defense mechanisms in adherence to type 2 diabetes regimens. Specifically, hetero-directed hostility may relate to an unconscious process, according to which disease-related painful feelings are displaced onto care relationships with negative impacts on adherence. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=alexithymia" title="alexithymia">alexithymia</a>, <a href="https://publications.waset.org/abstracts/search?q=defense%20mechanisms" title=" defense mechanisms"> defense mechanisms</a>, <a href="https://publications.waset.org/abstracts/search?q=treatment%20adherence" title=" treatment adherence"> treatment adherence</a>, <a href="https://publications.waset.org/abstracts/search?q=type%202%20diabetes%20mellitus" title=" type 2 diabetes mellitus"> type 2 diabetes mellitus</a> </p> <a href="https://publications.waset.org/abstracts/93212/the-role-of-defense-mechanisms-in-treatment-adherence-in-type-2-diabetes-mellitus-an-exploratory-study" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/93212.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">319</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">514</span> Time Series Simulation by Conditional Generative Adversarial Net</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rao%20Fu">Rao Fu</a>, <a href="https://publications.waset.org/abstracts/search?q=Jie%20Chen"> Jie Chen</a>, <a href="https://publications.waset.org/abstracts/search?q=Shutian%20Zeng"> Shutian Zeng</a>, <a href="https://publications.waset.org/abstracts/search?q=Yiping%20Zhuang"> Yiping Zhuang</a>, <a href="https://publications.waset.org/abstracts/search?q=Agus%20Sudjianto"> Agus Sudjianto</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Generative Adversarial Net (GAN) has proved to be a powerful machine learning tool in image data analysis and generation. In this paper, we propose to use Conditional Generative Adversarial Net (CGAN) to learn and simulate time series data. The conditions include both categorical and continuous variables with different auxiliary information. Our simulation studies show that CGAN has the capability to learn different types of normal and heavy-tailed distributions, as well as dependent structures of different time series. It also has the capability to generate conditional predictive distributions consistent with training data distributions. We also provide an in-depth discussion on the rationale behind GAN and the neural networks as hierarchical splines to establish a clear connection with existing statistical methods of distribution generation. In practice, CGAN has a wide range of applications in market risk and counterparty risk analysis: it can be applied to learn historical data and generate scenarios for the calculation of Value-at-Risk (VaR) and Expected Shortfall (ES), and it can also predict the movement of the market risk factors. We present a real data analysis including a backtesting to demonstrate that CGAN can outperform Historical Simulation (HS), a popular method in market risk analysis to calculate VaR. CGAN can also be applied in economic time series modeling and forecasting. In this regard, we have included an example of hypothetical shock analysis for economic models and the generation of potential CCAR scenarios by CGAN at the end of the paper. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=conditional%20generative%20adversarial%20net" title="conditional generative adversarial net">conditional generative adversarial net</a>, <a href="https://publications.waset.org/abstracts/search?q=market%20and%20credit%20risk%20management" title=" market and credit risk management"> market and credit risk management</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20network" title=" neural network"> neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=time%20series" title=" time series"> time series</a> </p> <a href="https://publications.waset.org/abstracts/123535/time-series-simulation-by-conditional-generative-adversarial-net" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/123535.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">143</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">513</span> Domain Adaptation Save Lives - Drowning Detection in Swimming Pool Scene Based on YOLOV8 Improved by Gaussian Poisson Generative Adversarial Network Augmentation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Simiao%20Ren">Simiao Ren</a>, <a href="https://publications.waset.org/abstracts/search?q=En%20Wei"> En Wei</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Drowning is a significant safety issue worldwide, and a robust computer vision-based alert system can easily prevent such tragedies in swimming pools. However, due to domain shift caused by the visual gap (potentially due to lighting, indoor scene change, pool floor color etc.) between the training swimming pool and the test swimming pool, the robustness of such algorithms has been questionable. The annotation cost for labeling each new swimming pool is too expensive for mass adoption of such a technique. To address this issue, we propose a domain-aware data augmentation pipeline based on Gaussian Poisson Generative Adversarial Network (GP-GAN). Combined with YOLOv8, we demonstrate that such a domain adaptation technique can significantly improve the model performance (from 0.24 mAP to 0.82 mAP) on new test scenes. As the augmentation method only require background imagery from the new domain (no annotation needed), we believe this is a promising, practical route for preventing swimming pool drowning. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=computer%20vision" title="computer vision">computer vision</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=YOLOv8" title=" YOLOv8"> YOLOv8</a>, <a href="https://publications.waset.org/abstracts/search?q=detection" title=" detection"> detection</a>, <a href="https://publications.waset.org/abstracts/search?q=swimming%20pool" title=" swimming pool"> swimming pool</a>, <a href="https://publications.waset.org/abstracts/search?q=drowning" title=" drowning"> drowning</a>, <a href="https://publications.waset.org/abstracts/search?q=domain%20adaptation" title=" domain adaptation"> domain adaptation</a>, <a href="https://publications.waset.org/abstracts/search?q=generative%20adversarial%20network" title=" generative adversarial network"> generative adversarial network</a>, <a href="https://publications.waset.org/abstracts/search?q=GAN" title=" GAN"> GAN</a>, <a href="https://publications.waset.org/abstracts/search?q=GP-GAN" title=" GP-GAN"> GP-GAN</a> </p> <a href="https://publications.waset.org/abstracts/163443/domain-adaptation-save-lives-drowning-detection-in-swimming-pool-scene-based-on-yolov8-improved-by-gaussian-poisson-generative-adversarial-network-augmentation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/163443.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">101</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span 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