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Permanent URI for this collectionhttps://hdl.handle.net/20.500.14288/3
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Publication Metadata only The role of endometrial sampling before Hysterectomy in premenopausal women with abnormal uterine bleeding(MDPI, 2024) Kuru, Oguzhan; Erkan, Ipek Betul Ozcivit; Saricoban, Cansu Turker; Akgor, Utku; Ilvan, Sennur; Department of Computer Engineering; İnan, Neslihan Gökmen; Department of Computer Engineering; College of EngineeringBackground/Objectives: An endometrial sampling is recommended for patients experiencing abnormal uterine bleeding above the age of 40 or 45. Valid risk prediction models are needed to accurately assess the risk of endometrial cancer and avoid an unnecessary endometrial biopsy in premenopausal women. We aimed to assess the necessity and usefulness of preoperative endometrial sampling by evaluating premenopausal women who underwent hysterectomy for abnormal uterine bleeding after preoperative endometrial sampling at our clinic. Methods: A retrospective analysis was conducted on 339 patients who underwent preoperative endometrial sampling and subsequently underwent hysterectomy due to abnormal uterine bleeding. Detailed gynecologic examinations, patient histories, and reports of endometrial sampling and hysterectomy were recorded. Cohen's Kappa (kappa) statistic was utilized to evaluate the concordance between histopathological results from an endometrial biopsy and hysterectomy. Results: The mean age of the cohort was 47 +/- 4 years. Endometrial biopsies predominantly revealed benign findings, with 137 (40.4%) cases showing proliferative endometrium and 2 (0.6%) cases showing endometrial cancer. Following hysterectomy, final pathology indicated proliferative endometrium in 208 (61.4%) cases, with 7 (2.1%) cases showing endometrioid cancer. There was a statistically significant but low level of concordance between histopathological reports of endometrial biopsy and hysterectomy results (Kappa = 0.108; p < 0.001). Significant differences were observed only in the body mass index of patients based on hysterectomy results (p = 0.004). When demographic characteristics were compared with cancer incidence, smoking status and preoperative endometrial biopsy findings showed statistically significant differences (p = 0.042 and p = 0.010, respectively). Conclusions: The concordance between the pathological findings of a preoperative endometrial biopsy and hysterectomy is low. Body mass index is an important differentiating factor between benign histopathologic findings of endometrium and endometrial neoplasia. Moreover, adenomyosis was found to be associated with endometrial cancer cases. The current approach to premenopausal women with abnormal uterine bleeding, which includes a routine endometrial biopsy, warrants re-evaluation by international societies and experts.Publication Metadata only HiSEG: Human assisted instance segmentation(Elsevier Ltd, 2024) Department of Computer Engineering; Sezgin, Tevfik Metin; Department of Computer Engineering; College of Engineering; Graduate School of Sciences and EngineeringInstance segmentation is a form of image detection which has a range of applications, such as object refinement, medical image analysis, and image/video editing, all of which demand a high degree of accuracy. However, this precision is often beyond the reach of what even state-of-the-art, fully automated instance segmentation algorithms can deliver. The performance gap becomes particularly prohibitive for small and complex objects. Practitioners typically resort to fully manual annotation, which can be a laborious process. In order to overcome this problem, we propose a novel approach to enable more precise predictions and generate higher-quality segmentation masks for high-curvature, complex and small-scale objects. Our human-assisted segmentation method, HiSEG, augments the existing Strong Mask R-CNN network to incorporate human-specified partial boundaries. We also present a dataset of hand-drawn partial object boundaries, which we refer to as “human attention maps”. In addition, the Partial Sketch Object Boundaries (PSOB) dataset contains hand-drawn partial object boundaries which represent curvatures of an object's ground truth mask with several pixels. Through extensive evaluation using the PSOB dataset, we show that HiSEG outperforms state-of-the art methods such as Mask R-CNN, Strong Mask R-CNN, Mask2Former, and Segment Anything, achieving respective increases of +42.0, +34.9, +29.9, and +13.4 points in APMask metrics for these four models. We hope that our novel approach will set a baseline for future human-aided deep learning models by combining fully automated and interactive instance segmentation architectures.Publication Metadata only Affect burst detection using multi-modal cues(IEEE, 2015) Department of Computer Engineering; Department of Computer Engineering; N/A; Department of Computer Engineering; N/A; Sezgin, Tevfik Metin; Yemez, Yücel; Türker, Bekir Berker; Erzin, Engin; Marzban, Shabbir; Faculty Member; Faculty Member; PhD Student; Faculty Member; Master Student; Department of Computer Engineering; College of Engineering; College of Engineering; Graduate School of Sciences and Engineering; College of Engineering; Graduate School of Sciences and Engineering; 18632; 107907; N/A; 34503; N/ARecently, affect bursts have gained significant importance in the field of emotion recognition since they can serve as prior in recognising underlying affect bursts. In this paper we propose a data driven approach for detecting affect bursts using multimodal streams of input such as audio and facial landmark points. The proposed Gaussian Mixture Model based method learns each modality independently followed by combining the probabilistic outputs to form a decision. This gives us an edge over feature fusion based methods as it allows us to handle events when one of the modalities is too noisy or not available. We demonstrate robustness of the proposed approach on 'Interactive emotional dyadic motion capture database' (IEMOCAP) which contains realistic and natural dyadic conversations. This database is annotated by three annotators to segment and label affect bursts to be used for training and testing purposes. We also present performance comparison between SVM based methods and GMM based methods for the same configuration of experiments.Publication Metadata only Artificial bandwidth extension of speech excitation(IEEE, 2015) Department of Computer Engineering; N/A; Erzin, Engin; Turan, Mehmet Ali Tuğtekin; Faculty Member; PhD Student; Department of Computer Engineering; College of Engineering; Graduate School of Sciences and Engineering; 34503; N/AIn this paper, a new approach that extends narrowband excitation signals to synthesize wide-band speech have been proposed. Bandwidth extension problem is analyzed using source-filter separation framework where a speech signal is decomposed into two independent components. For spectral envelope extension, our former work based on hidden Markov model have been used. For excitation signal extension, the proposed method moves the spectrum based on correlation analysis where the distance between the harmonics and the structure of the excitation signal are preserved in high-bands. In experimental studies, we also apply two other well-known extension techniques for excitation signals comparatively and evaluate the overall performance of proposed system using the PESQ metric. Our findings indicate that the proposed extension method outperforms other two techniques. © 2015 IEEE./ Öz: Bu çalışmada dar bantlı kaynak sinyallerinin bant genişliği artırılarak geniş bantlı konuşma sentezleyen yeni bir yaklaşım önerilmektedir. Bant genişletme problemi kaynak süzgeç analizinin yardımıyla iki bağımsız bileşen üzerinde ayrı ayrı ele alınmıştır. Süzgeç yapısını şekillendiren izgesel zarfı, saklı Markov modeli tabanlı geçmiş çalışmamızı kullanarak iyileştirirken, dar bantlı kaynak sinyalinin genişletilmesi için izgesel kopyalamaya dayalı yeni bir yöntem öneriyoruz. Bu yeni yöntemde dar bantlı kaynak sinyalinin yüksek frekans bileşenlerindeki harmonik yapısını, ilinti analizi ile genişletip geniş bantlı kaynak sinyali sentezlemekteyiz. Öne sürülen bu iyileştirmenin başarımını ölçebilmek için literatürde sıklıkla kullanılan iki ayrı genişletme yöntemi de karşılaştırmalı olarak degerlendirilmekte- dir. Deneysel çalışmalarda öne sürdüğümüz genişletmenin PESQ ölçütüyle nesnel başarımı gösterilmiştir.Publication Metadata only Run-time verification of optimistic concurrency(Springer, 2010) Qadeer, Shaz; N/A; Department of Computer Engineering; Department of Computer Engineering; Sezgin, Ali; Taşıran, Serdar; Muşlu, Kıvanç; Researcher; Faculty Member; Undergraduate Student; Department of Computer Engineering; N/A; College of Engineering; College of Engineering; N/A; N/A; N/AAssertion based specifications are not suitable for optimistic concurrency where concurrent operations are performed assuming no conflict among threads and correctness is cast in terms of the absence or presence of conflicts that happen in the future. What is needed is a formalism that allows expressing constraints about the future. In previous work, we introduced tressa claims and incorporated prophecy variables as one such formalism. We investigated static verification of tressa claims and how tressa claims improve reduction proofs. In this paper, we consider tressa claims in the run-time verification of optimistic concurrency implementations. We formalize, via a simple grammar, the annotation of a program with tressa claims. Our method relieves the user from dealing with explicit manipulation of prophecy variables. We demonstrate the use of tressa claims in expressing complex properties with simple syntax. We develop a run-time verification framework which enables the user to evaluate the correctness of tressa claims. To this end, we first describe the algorithms for monitor synthesis which can be used to evaluate the satisfaction of a tressa claim over a given execution. We then describe our tool implementing these algorithms. We report our initial test results.Publication Metadata only Using haptics to convey cause-and-effect relations in climate visualization(IEEE, 2008) Sen, Omer Lutfi; Department of Mechanical Engineering; Department of Computer Engineering; Başdoğan, Çağatay; Taşıran, Serdar; Yannier, Nesra; Faculty Member; Faculty Member; Master Student; Department of Mechanical Engineering; Department of Computer Engineering; College of Engineering; College of Engineering; Graduate School of Sciences and Engineering; 125489; N/A; N/AWe investigate the potential role of haptics in augmenting the visualization of climate data. In existing approaches to climate visualization, dimensions of climate data such as temperature, humidity, wind, precipitation, and cloud water are typically represented using different visual markers and dimensions such as color, size, intensity, and orientation. Since the numbers of dimensions in climate data are large and climate data need to be represented in connection with the topography, purely visual representations typically overwhelm users. Rather than overloading the visual channel, we investigate an alternative approach in which some of the climate information is displayed through the haptic channel in order to alleviate the perceptual and cognitive load of the user. In this approach, haptic feedback is further used to provide guidance while exploring climate data in order to enable natural and intuitive learning of cause-and-effect relationships between climate variables. As the user explores climate data interactively under the guidance of wind forces displayed by a haptic device, she/he can understand better the occurrence of events such as cloud and rain formation and the effect of climate variables on these events. We designed a set of experiments to demonstrate the effectiveness of this multimodal approach. Our experiments with 33 human subjects show that haptic feedback significantly improves the understanding of climate data and the cause-and-effect relations between climate variables, as well as the interpretation of the variations in climate due to changes in terrain. © 2008 IEEE.Publication Metadata only Dynamic management of control plane performance in software-defined networks(Institute of Electrical and Electronics Engineers (IEEE), 2016) Görkemli, Burak; Parlakışık, A. Murat; Civanlar, Seyhan; Ulaş, Aydın; Department of Electrical and Electronics Engineering; Tekalp, Ahmet Murat; Faculty Member; Department of Electrical and Electronics Engineering; College of Engineering; 26207The controller or the control plane is at the heart of software defined networks (SDN). As SDN migrates to wide area networks (WAN), scalability and performance are two important factors that differentiate one controller from another, and they are critical for success of SDN for end-to-end service management. We distinguish control flows from data flows, and introduce a novel dynamic control plane architecture to distribute different control flows among multiple controller instances depending on specific controller load and controller processor utilization or on the data flow service type. We propose control flow tables - a concept introduced in this paper - that are embedded in OpenFlow flow tables to distribute the control flows among various controller instances. Experimental results demonstrate the improvements in the data plane service performance as a result of the proposed control flow management procedures when the bottleneck is the controller CPU or throughput of links between the controller and switches.Publication Metadata only Stepwise probabilistic buffering for epidemic information dissemination(Institute of Electrical and Electronics Engineers (IEEE), 2006) N/A; Department of Mathematics; Department of Computer Engineering; Ahi, Emrah; Çağlar, Mine; Özkasap, Öznur; Master Student; Faculty Member; Faculty Member; Department of Mathematics; Department of Computer Engineering; Graduate School of Sciences and Engineering; College of Sciences; College of Engineering; N/A; 105131; 113507For large-scale peer-to-peer applications, bioinspired epidemic protocols have considerable advantages as they are robust against network failures, scalable and provide probabilistic reliability guarantees. While providing reliability, a key issue to consider is the usage of system wide buffer space. In this context, we introduce a novel scheme called stepwise probabilistic buffering that reduces the amount of buffering and distributes the load of buffering to the entire system where every peer does not have the complete view of the system. We compute the performance measures through simulations of large-scale application scenarios.Publication Metadata only Comparison of phoneme and viseme based acoustic units for speech driven realistic lip animation(IEEE, 2007) Bozkurt, Elif; Erdem, Çiǧdem Eroǧlu; Erdem, Tanju; Özkan, Mehmet; Department of Computer Engineering; Erzin, Engin; Faculty Member; Department of Computer Engineering; College of Engineering; 34503Natural looking lip animation, synchronized with incoming speech, is essential for realistic character animation. In this work, we evaluate the performance of phone and viseme based acoustic units, with and without context information, for generating realistic lip synchronization using HMM based recognition systems. We conclude via objective evaluations that utilization of viseme based units with context information outperforms the other methods./ Öz: Konuşma ile senkronize ve doğal görünen dudak hareketlerinin üretilmesi, gerçekçi karakter animasyonu için önemli bir problemdir. Bu çalışmada, gerçekçi dudak hareketleri üretebilmek için Saklı Markov Modeli (SMM) kullanarak, fonem ve vizem temelli akustik birimlerin başarımlarını karşılaştırıyoruz. Nesnel değerlendirmeler sonucunda, komşuluk bilgisini kullanan vizem temelli akustik birimlerin diğer metodlardan daha üstün olduğunu gösteriyoruz.Publication Metadata only Seed-based distributed group key selection algorithm for ad Hoc networks(IEEE, 2007) N/A; Department of Computer Engineering; N/A; Özkasap, Öznur; Atsan, Emre; Faculty Member; Master Student; Department of Computer Engineering; College of Engineering; Graduate School of Sciences and Engineering; 113507; N/AKey establishment has a significant role in providing secure infrastructure for ad hoc networks. For this purpose, several key pre-distribution schemes have been proposed, but majority of the existing schemes rely on a trusted third party which causes a constraint in ad hoc platforms. We propose a seed-based distributed key selection algorithm, namely SeeDKS, for groups of nodes in ad hoc networks. Our approach is inspired by the earlier work on distributed key selection (DKS) and is based on the idea of common group key pool generated with group seed value for each different group. Simulation results show that using very small key ring sizes compared to DKS, we can achieve satisfactory results which DKS cannot accomplish in means of finding at least one common key among group members.