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Permanent URI for this collectionhttps://hdl.handle.net/20.500.14288/3
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Publication Metadata only Spatial and thermal aware methods for efficient workload management in distributed data centers(Elsevier B.V., 2024) N/A; Department of Computer Engineering; Ali, Ahsan; Özkasap, Öznur; Department of Computer Engineering; Graduate School of Sciences and Engineering; College of EngineeringGeographically distributed data centers provide facilities for users to fulfill the demand of storage and computations, where most of the operational cost is due to electricity consumption. In this study, we address the problem of energy consumption of cloud data centers and identify key characteristics of techniques proposed for reducing operational costs, carbon emissions, and financial penalties due to service level agreement (SLA) violations. By considering computer room air condition (CRAC) units that utilize outside air for cooling purposes as well as temperature and space-varying properties, we propose the energy cost model which takes into account temperature ranges for cooling purposes and operations of CRAC units. Then, we propose spatio-thermal-aware algorithms to manage workload using the variation of electricity price, locational outside and within the data center temperature, where the aim is to schedule the incoming workload requests with minimum SLA violations, cooling cost, and energy consumption. We analyzed the performance of our proposed algorithms and compared the experimental results with the benchmark algorithms for metrics of interest including SLA violations, cooling cost, and overall operations cost. Modeling, experiments, and verification conducted on CloudSim with realistic data center scenarios and workload traces show that the proposed algorithms result in reduced SLA violations, save between 15% to 75% of cooling cost and between 3.89% to 39% of the overall operational cost compared to the existing solutions.Publication Metadata only Hyperspectral image denoising via self-modulating convolutional neural networks(Elsevier B.V., 2024) Torun, Orhan; Yuksel, Seniha Esen; Imamoglu, Nevrez; Department of Computer Engineering; Erdem, Aykut; Department of Computer Engineering; College of EngineeringCompared to natural images, hyperspectral images (HSIs) consist of a large number of bands, with each band capturing different spectral information from a certain wavelength, even some beyond the visible spectrum. These characteristics of HSIs make them highly effective for remote sensing applications. That said, the existing hyperspectral imaging devices introduce severe degradation in HSIs. Hence, hyperspectral image denoising has attracted lots of attention by the community lately. While recent deep HSI denoising methods have provided effective solutions, their performance under real-life complex noise remains suboptimal, as they lack adaptability to new data. To overcome these limitations, in our work, we introduce a self-modulating convolutional neural network which we refer to as SM-CNN, which utilizes correlated spectral and spatial information. At the core of the model lies a novel block, which we call spectral self-modulating residual block (SSMRB), that allows the network to transform the features in an adaptive manner based on the adjacent spectral data, enhancing the network's ability to handle complex noise. In particular, the introduction of SSMRB transforms our denoising network into a dynamic network that adapts its predicted features while denoising every input HSI with respect to its spatio-spectral characteristics. Experimental analysis on both synthetic and real data shows that the proposed SM-CNN outperforms other state-of-the-art HSI denoising methods both quantitatively and qualitatively on public benchmark datasets. Our code will be available at https://github.com/orhan-t/SM-CNN.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 Object and relation centric representations for push effect prediction(Elsevier, 2024) Tekden, Ahmet E.; Asfour, Tamim; Uğur, Emre; Department of Computer Engineering; Erdem, Aykut; Department of Computer Engineering; College of EngineeringPushing is an essential non -prehensile manipulation skill used for tasks ranging from pre -grasp manipulation to scene rearrangement, reasoning about object relations in the scene, and thus pushing actions have been widely studied in robotics. The effective use of pushing actions often requires an understanding of the dynamics of the manipulated objects and adaptation to the discrepancies between prediction and reality. For this reason, effect prediction and parameter estimation with pushing actions have been heavily investigated in the literature. However, current approaches are limited because they either model systems with a fixed number of objects or use image -based representations whose outputs are not very interpretable and quickly accumulate errors. In this paper, we propose a graph neural network based framework for effect prediction and parameter estimation of pushing actions by modeling object relations based on contacts or articulations. Our framework is validated both in real and simulated environments containing different shaped multi -part objects connected via different types of joints and objects with different masses, and it outperforms image -based representations on physics prediction. Our approach enables the robot to predict and adapt the effect of a pushing action as it observes the scene. It can also be used for tool manipulation with never -seen tools. Further, we demonstrate 6D effect prediction in the lever -up action in the context of robot -based hard -disk disassembly.Publication Metadata only A simplified grid method of camera-captured images may be a practical alternative if validated ai-assisted counting is inaccessible(Elsevier Science Inc, 2023) Adsay, David; Eren, Ozgur; Basturk, Olca; Department of Computer Engineering; Esmer, Rohat; Armutlu, Ayşe; Taşkın, Orhun Çığ; Koç, Soner; Tezcan, Nuray; Aktaş, Berk Kaan; Kulaç, İbrahim; Kapran, Yersu; Demir, Çiğdem Gündüz; Saka, Burcu; Department of Computer Engineering; School of Medicine; Graduate School of Sciences and Engineering; College of EngineeringN/APublication Metadata only An evaluation of DNA methylation levels and sleep in relation to hot flashes: a cross-sectional study(MDPI, 2024) Ozcivit Erkan, Ipek Betul; Seyisoglu, Hasan Hakan; Senel, Gulcin Benbir; Karadeniz, Derya; Ozdemir, Filiz; Kalayci, Aysel; Seven, Mehmet; Department of Computer Engineering; İnan, Neslihan Gökmen; Department of Computer Engineering; College of EngineeringObjectives: We aimed to evaluate the DNA methylation levels in perimenopausal and postmenopausal women, measured through Long Interspersed Element-1 (LINE-1) and Alu, and the sleep parameters in relation to the presence of hot flashes (HFs). Methods: This cross-sectional study included 30 peri- or postmenopausal women aged between 45 and 55. The menopausal status was determined according to STRAW + 10 criteria and all participants had a low cardiovascular disease (CVD) risk profile determined by Framingham risk score. The sample was divided into two groups based on the presence or absence of HFs documented in their medical history during their initial visit: Group 1 (n = 15) with HFs present and Group 2 (n = 15) with HFs absent. The patients had polysomnography test and HFs were recorded both by sternal skin conductance and self-report overnight. Genomic DNA was extracted from the women's blood and methylation status was analyzed by fluorescence-based real-time quantitative PCR. The quantified value of DNA methylation of a target gene was normalized by beta-actin. The primary outcome was the variation in methylation levels of LINE-1 and Alu and sleep parameters according to the presence of HFs. Results: LINE-1 and Alu methylation levels were higher in Group 1 (HFs present), although statistically non-significant. LINE-1 methylation levels were negatively correlated with age. Sleep efficiency was statistically significantly lower for women in Group 1 (HFs present) (74.66% +/- 11.16% vs. 82.63% +/- 7.31%;p = 0.03). The ratio of duration of awakening to total sleep time was statistically significantly higher in Group 1 (HFs present) (22.38% +/- 9.99% vs. 15.07% +/- 6.93, p = 0.03). Objectively recorded hot flashes were significantly higher in Group 1 (4.00 +/- 3.21 vs. 1.47 +/- 1.46, p = 0.03). None of the cases in Group 2 self-reported HF despite objectively recorded HFs during the polysomnography. The rate of hot flash associated with awakening was 41.4% in the whole sample. Conclusions: Women with a history of hot flashes exhibited lower sleep efficiency and higher awakening rates. Although a history of experiencing hot flashes was associated with higher LINE-1 and Alu methylation levels, no statistical significance was found. Further studies are needed to clarify this association. This study was funded by the Scientific Research Projects Coordination Unit of Istanbul University-Cerrahpasa. Project number: TTU-2021-35629.Publication Metadata only Omnidirectional image quality assessment with local-global vision transformers(Elsevier, 2024) Elfkir, Mohamed Hedi; Imamoglu, Nevrez; Ozcinar, Cagri; Erdem, Erkut; Department of Computer Engineering; Tofighi, Nafiseh Jabbari; Erdem, Aykut; Department of Computer Engineering; Koç Üniversitesi İş Bankası Yapay Zeka Uygulama ve Araştırma Merkezi (KUIS AI)/ Koç University İş Bank Artificial Intelligence Center (KUIS AI); Graduate School of Sciences and Engineering; College of EngineeringWith the rising popularity of omnidirectional images (ODIs) in virtual reality applications, the need for specialized image quality assessment (IQA) methods becomes increasingly critical. Traditional IQA approaches, designed for rectilinear images, often fail to evaluate ODIs accurately due to their 360 -degree scene representation. Addressing this, we introduce the Local - Global Transformer for 360 -degree Image Quality Assessment (LGT360IQ). This novel framework features dual branches tailored to mimic top -down and bottom -up visual attention mechanisms, adapted for the spherical characteristics of ODIs. The local branch processes tangent viewports from salient regions within the equirectangular projection image, extracting detailed features for granular quality assessment. In parallel, the global branch utilizes a task -dependent token sampling strategy for holistic image feature processing and quality score prediction. This integrated approach combines local and global information, offering an effective IQA method for ODIs. Our extensive evaluation across three benchmark ODI datasets, CVIQ, OIQA, and ODI, demonstrates LGT360IQ superior performance and establishes its role in advancing the field of IQA for omnidirectional images.Publication Metadata only HyperE2VID: improving event-based video reconstruction via hypernetworks(IEEE-Inst Electrical Electronics Engineers Inc, 2024) Ercan, Burak; Eker, Onur; Sağlam, Canberk; Erdem, Erkut; Department of Computer Engineering; Erdem, Aykut; Department of Computer Engineering; Koç Üniversitesi İş Bankası Enfeksiyon Hastalıkları Uygulama ve Araştırma Merkezi (EHAM) / Koç University İşbank Center for Infectious Diseases (KU-IS CID); College of Engineering;Event-based cameras are becoming increasingly popular for their ability to capture high-speed motion with low latency and high dynamic range. However, generating videos from events remains challenging due to the highly sparse and varying nature of event data. To address this, in this study, we propose HyperE2VID, a dynamic neural network architecture for event-based video reconstruction. Our approach uses hypernetworks to generate per-pixel adaptive filters guided by a context fusion module that combines information from event voxel grids and previously reconstructed intensity images. We also employ a curriculum learning strategy to train the network more robustly. Our comprehensive experimental evaluations across various benchmark datasets reveal that HyperE2VID not only surpasses current state-of-the-art methods in terms of reconstruction quality but also achieves this with fewer parameters, reduced computational requirements, and accelerated inference times.Publication Metadata only Byzantines can also learn from history: fall of centered clipping in federated learning(IEEE-Inst Electrical Electronics Engineers Inc, 2024) Özfatura, Emre; Gündüz, Deniz; Department of Computer Engineering; Özfatura, Ahmet Kerem; Küpçü, Alptekin; Department of Computer Engineering; Koç Üniversitesi İş Bankası Enfeksiyon Hastalıkları Uygulama ve Araştırma Merkezi (EHAM) / Koç University İşbank Center for Infectious Diseases (KU-IS CID); Graduate School of Sciences and Engineering; College of Engineering;The increasing popularity of the federated learning (FL) framework due to its success in a wide range of collaborative learning tasks also induces certain security concerns. Among many vulnerabilities, the risk of Byzantine attacks is of particular concern, which refers to the possibility of malicious clients participating in the learning process. Hence, a crucial objective in FL is to neutralize the potential impact of Byzantine attacks and to ensure that the final model is trustable. It has been observed that the higher the variance among the clients' models/updates, the more space there is for Byzantine attacks to be hidden. As a consequence, by utilizing momentum, and thus, reducing the variance, it is possible to weaken the strength of known Byzantine attacks. The centered clipping (CC) framework has further shown that the momentum term from the previous iteration, besides reducing the variance, can be used as a reference point to neutralize Byzantine attacks better. In this work, we first expose vulnerabilities of the CC framework, and introduce a novel attack strategy that can circumvent the defences of CC and other robust aggregators and reduce their test accuracy up to %33 on best-case scenarios in image classification tasks. Then, we propose a new robust and fast defence mechanism that is effective against the proposed and other existing Byzantine attacks.Publication Metadata only AffectON: Incorporating affect into dialog generation(IEEE-Inst Electrical Electronics Engineers Inc, 2023) Bucinca, Zana; Department of Computer Engineering; Yemez, Yücel; Erzin, Engin; Sezgin, Tevfik Metin; Department of Computer Engineering; Koç Üniversitesi İş Bankası Yapay Zeka Uygulama ve Araştırma Merkezi (KUIS AI)/ Koç University İş Bank Artificial Intelligence Center (KUIS AI); College of EngineeringDue to its expressivity, natural language is paramount for explicit and implicit affective state communication among humans. The same linguistic inquiry (e.g., How are you?) might induce responses with different affects depending on the affective state of the conversational partner(s) and the context of the conversation. Yet, most dialog systems do not consider affect as constitutive aspect of response generation. In this article, we introduce AffectON, an approach for generating affective responses during inference. For generating language in a targeted affect, our approach leverages a probabilistic language model and an affective space. AffectON is language model agnostic, since it can work with probabilities generated by any language model (e.g., sequence-to-sequence models, neural language models, n-grams). Hence, it can be employed for both affective dialog and affective language generation. We experimented with affective dialog generation and evaluated the generated text objectively and subjectively. For the subjective part of the evaluation, we designed a custom user interface for rating and provided recommendations for the design of such interfaces. The results, both subjective and objective demonstrate that our approach is successful in pulling the generated language toward the targeted affect, with little sacrifice in syntactic coherence.