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Yayın A point cloud filtering method based on anisotropic error model(John Wiley and Sons Inc, 2023-12) Özendi, Mustafa; Akça, Devrim; Topan, HüseyinMany modelling applications require 3D meshes that should be generated from filtered/cleaned point clouds. This paper proposes a methodology for filtering of terrestrial laser scanner (TLS)-derived point clouds, consisting of two main parts: an anisotropic point error model and the subsequent decimation steps for elimination of low-quality points. The point error model can compute the positional quality of any point in the form of error ellipsoids. It is formulated as a function of the angular/mechanical stability, sensor-to-object distance, laser beam's incidence angle and surface reflectivity, which are the most dominant error sources. In a block of several co-registered point clouds, some parts of the target object are sampled by multiple scans with different positional quality patterns. This situation results in redundant data. The proposed decimation steps removes this redundancy by selecting only the points with the highest positional quality. Finally, the Good, Bad, and the Better algorithm, based on the ray-tracing concept, was developed to remove the remaining redundancy due to the Moiré effects. The resulting point cloud consists of only the points with the highest positional quality while reducing the number of points by factor 10. This novel approach resulted in final surface meshes that are accurate, contain predefined level of random errors and require almost no manual intervention.Yayın Maintenance policy analysis of the regenerative air heater system using factored POMDPs(Elsevier Ltd, 2022-03) Kıvanç, İpek; Özgür Ünlüakın, Demet; Bilgiç, TanerMaintenance optimization of multi-component systems is a difficult problem. Partially Observable Markov Decision Processes (POMDPs) are powerful tools for such problems under uncertainty in stochastic environments. In this study, the main POMDP solution approaches and solvers are surveyed. Then, based on experimental models with different complexities in the size of the system space, selected POMDP solvers using different representation patterns for modeling and different procedures for updating the value function while solving are compared. Furthermore, to show that factored representations are advantageous in modeling and solving the maintenance problem of multi-component systems where there exist also stochastic dependencies among the components, the maintenance problem of the one-line regenerative air heater system available in thermal power plants is modeled and solved with factored POMDPs. In-depth sensitivity analyses are performed on the obtained policy. The results show that factored POMDPs enable compact modeling, efficient policy generation and practical policy analysis for the tackled problem. Furthermore, they also motivate the use of factored POMDPs in the generation and analysis of maintenance policies for similar multi-component systems.Yayın A stochastic risk-averse framework for blood donation appointment scheduling under uncertain donor arrivals(Springer, 2020-12) Yalçındağ, Semih; Baş Güre, Seda; Carello, Giuliana; Lanzarone, EttoreBlood is a key resource in all health care systems, usually drawn from voluntary donors. We focus on the operations management in blood collection centers, which is a key step to guarantee an adequate blood supply and a good quality of service to donors, by addressing the so-called Blood Donation Appointment Scheduling problem. Its goal is to employ appointment scheduling to balance the production of blood units between days, in order to provide a reasonably constant supply to transfusion centers and hospitals, and reduce non-alignments between physicians' working times and donor arrivals at the collection center. We consider a two-phase solution framework taken from the literature, in which a deterministic linear programming model preallocates time slots to different blood types and a prioritization policy assigns the preallocated slots to the donors when they make a reservation. However, the problem is stochastic in nature and requires consideration of the uncertain arrivals of non-booked donors. In this work, to include the uncertain arrivals, we propose three stochastic counterparts of the preallocation model based on a risk-neutral objective and two risk-averse objectives, respectively, where the Conditional Value-at-Risk is considered as the risk measure in the last two methods. The resulting stochastic frameworks have been tested considering the historical data of one of the largest Italian collection centers, the Milan Department of the "Associazione Volontari Italiani Sangue" (AVIS). Results show the effectiveness of the stochastic models, especially the mean-risk one, and the need to include the uncertainty of arrivals in order to better balance the production of blood units.Yayın Closeness and uncertainty aware adversarial examples detection in adversarial machine learning(Elsevier Ltd, 2022-07) Tuna, Ömer Faruk; Çatak, Ferhat Özgür; Eskil, Mustafa TanerWhile deep learning models are thought to be resistant to random perturbations, it has been demonstrated that these architectures are vulnerable to deliberately crafted perturbations, albeit being quasi-imperceptible. These vulnerabilities make it challenging to deploy Deep Neural Network (DNN) models in security-critical areas. Recently, many research studies have been conducted to develop defense techniques enabling more robust models. In this paper, we target detecting adversarial samples by differentiating them from their clean equivalents. We investigate various metrics for detecting adversarial samples. We first leverage moment-based predictive uncertainty estimates of DNN classifiers derived through Monte-Carlo (MC) Dropout Sampling. We also introduce a new method that operates in the subspace of deep features obtained by the model. We verified the effectiveness of our approach on different datasets. Our experiments show that these approaches complement each other, and combined usage of all metrics yields 99 % ROC-AUC adversarial detection score for well-known attack algorithms.Yayın Psychological distress of breast cancer survivors during the Covid-19 pandemic and related factors: a controlled study(KARE Publication, 2023-07) Taş, Beyza; Anuk, Dilek; Akçinar Yayla, BernaOBJECTIVE: Although the prevalence of breast cancer is high among women, survival rates are increasing. How-ever, breast cancer survivors (BCS) continue to experience various psychological problems after their treatments and are also exposed to additional stressors, such as the current Coronavirus disease 2019 (COVID-19) pandemic. The aim of this study was to examine the psychological distress and related factors (social support, intolerance of uncertainty, coping strategies) of BCS during the COVID-19 pandemic and the role of breast cancer diagnosis in this process. METHODS: This study included 95 BCS and 87 healthy women. Sociodemographic Information Form and depression anxiety stress scale, social support scale, intolerance of uncertainty scale, and coping strategies short form scales were administered to the participants. T tests and regression analyses were performed to examine the relationships between the variables. RESULTS: There was no significant difference between the two groups in terms of depression and anxiety, but the stress of BCS was lower than that of healthy women. In the regression analysis, the diagnosis of breast cancer was not a predictor for depression and anxiety, but it was a significant predictor for stress. Com-mon predictors of increased depression, anxiety, and stress were decreased social support, increased uncertainty intolerance, and increased emotion-focused coping. CONCLUSION: Focusing on the development of intolerance of uncertainty, social support, and problem-focused coping strategies of psychological interventions for women BCS during epidemics such as COVID-19 may reduce their psychological distress while maintaining and increasing their psychological well-being.Yayın TENET: a new hybrid network architecture for adversarial defense(Springer Science and Business Media Deutschland GmbH, 2023-08) Tuna, Ömer Faruk; Çatak, Ferhat Özgür; Eskil, Mustafa TanerDeep neural network (DNN) models are widely renowned for their resistance to random perturbations. However, researchers have found out that these models are indeed extremely vulnerable to deliberately crafted and seemingly imperceptible perturbations of the input, referred to as adversarial examples. Adversarial attacks have the potential to substantially compromise the security of DNN-powered systems and posing high risks especially in the areas where security is a top priority. Numerous studies have been conducted in recent years to defend against these attacks and to develop more robust architectures resistant to adversarial threats. In this study, we propose a new architecture and enhance a recently proposed technique by which we can restore adversarial samples back to their original class manifold. We leverage the use of several uncertainty metrics obtained from Monte Carlo dropout (MC Dropout) estimates of the model together with the model’s own loss function and combine them with the use of defensive distillation technique to defend against these attacks. We have experimentally evaluated and verified the efficacy of our approach on MNIST (Digit), MNIST (Fashion) and CIFAR10 datasets. In our experiments, we showed that our proposed method reduces the attack’s success rate lower than 5% without compromising clean accuracy.Yayın Son ergenlik döneminde belirsizliğe tahammülsüzlük ve aleksitimi arasındaki ilişkide anksiyetenin aracı etkisinin incelenmesi(Işık Üniversitesi, 2022-02-04) Özmen, Fatma Hilal; Aktan, Zekeriya Deniz; Işık Üniversitesi, Lisansüstü Eğitim Enstitüsü, Klinik Psikoloji Yüksek Lisans ProgramıBu araştırmanın temel amacı, son ergenlik dönemindeki bireylerde belirsizliğe tahammülsüzlük ve aleksitimi arasındaki ilişkide anksiyetenin aracı rolünü incelemektir. Araştırma kapsamında içleme ve dışlama kriterlerine uygun bulunan 430 katılımcının verisi ile analizler gerçekleştirilmiştir. Veri toplama aracı olarak kişilerin sosyodemografik bilgilerine ulaşmak için Kişisel Bilgi Formu, Belirsizliğe Tahammülsüzlük Ölçeği (BTÖ-12), Toronto Aleksitimi Ölçeği (TAÖ - 20) ve Beck Anksiyete Envanteri kullanılmıştır. Araştırmamızdaki temel hipotezleri test etmek için, basit regresyon ve doğrusal hiyerarşik regresyon analizleri kullanılmıştır. Araştırma sonucuna göre belirsizliğe tahammülsüzlük ile aleksitimi arasındaki ilişkide anksiyetenin anlamlı kısmi aracı etkisi (p=0,000) olduğu tespit edilmiştir p<0.05. Araştırma, belirsizliğe tahammülsüzlük ve aleksitimi arasındaki ilişkinin anksiyete aracılığı ile gerçekleştiğini ortaya koymuştur.Yayın Uncertainty as a Swiss army knife: new adversarial attack and defense ideas based on epistemic uncertainty(Springer, 2022-04-02) Tuna, Ömer Faruk; Çatak, Ferhat Özgür; Eskil, Mustafa TanerAlthough state-of-the-art deep neural network models are known to be robust to random perturbations, it was verified that these architectures are indeed quite vulnerable to deliberately crafted perturbations, albeit being quasi-imperceptible. These vulnerabilities make it challenging to deploy deep neural network models in the areas where security is a critical concern. In recent years, many research studies have been conducted to develop new attack methods and come up with new defense techniques that enable more robust and reliable models. In this study, we use the quantified epistemic uncertainty obtained from the model's final probability outputs, along with the model's own loss function, to generate more effective adversarial samples. And we propose a novel defense approach against attacks like Deepfool which result in adversarial samples located near the model's decision boundary. We have verified the effectiveness of our attack method on MNIST (Digit), MNIST (Fashion) and CIFAR-10 datasets. In our experiments, we showed that our proposed uncertainty-based reversal method achieved a worst case success rate of around 95% without compromising clean accuracy.Yayın Exploiting epistemic uncertainty of the deep learning models to generate adversarial samples(Springer, 2022-03) Tuna, Ömer Faruk; Çatak, Ferhat Özgür; Eskil, Mustafa TanerDeep neural network (DNN) architectures are considered to be robust to random perturbations. Nevertheless, it was shown that they could be severely vulnerable to slight but carefully crafted perturbations of the input, termed as adversarial samples. In recent years, numerous studies have been conducted in this new area called ``Adversarial Machine Learning” to devise new adversarial attacks and to defend against these attacks with more robust DNN architectures. However, most of the current research has concentrated on utilising model loss function to craft adversarial examples or to create robust models. This study explores the usage of quantified epistemic uncertainty obtained from Monte-Carlo Dropout Sampling for adversarial attack purposes by which we perturb the input to the shifted-domain regions where the model has not been trained on. We proposed new attack ideas by exploiting the difficulty of the target model to discriminate between samples drawn from original and shifted versions of the training data distribution by utilizing epistemic uncertainty of the model. Our results show that our proposed hybrid attack approach increases the attack success rates from 82.59% to 85.14%, 82.96% to 90.13% and 89.44% to 91.06% on MNIST Digit, MNIST Fashion and CIFAR-10 datasets, respectively.
