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Recent Submitted Publications

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Mock‐up versus CAD Modeling Preferences of Architecture Students in the Early Design Phase
(JCoDe: Journal of Computational Design, 2023) Samancı, Buket; Taşpınar, Özge; Karcı, Yaşar Emir; Cengiz, Başak; Özdoğan, Selen; Yıldız Özkan, Dilek; Bittermann, Michael S
Preferences for using physical mock-up modeling or computer-aided design (CAD) among architecture students in the early design phase are analyzed. The data is obtained from a questionnaire, consisting of eight multiple-choice questions and one open-ended question. The respondents are architecture students; the majority of them are still in their undergraduate studies. As quantitative analysis methods hypothesis tests based on the probability distributions known as the z-distribution, and the Chi-squared distribution were carried out. Generally, it was investigated which modeling technique is more efficient in the early design phase. Moreover, according to the age groups of respondents, the difference in the preference among mock-up and CAD is identified. Explicitly, younger students prefer CAD, while other ones prefer mock-up representation. The reasons for the difference are analyzed. Since the choice for mock-up modeling or CAD modeling can have a strong impact on the design processes of both, students and professionals, the result of the study is relevant, because it gives a hint about probable future architecture practice.
Conference Object
Weak Label Supervision for Monaural Source Separation Using Non-Negative Denoising Variational Autoencoders
(IEEE, 2019) Karamatli, Ertug; Kirbiz, Serap; Cemgil, Ali Taylan
Deep learning models are very effective in source separation when there are large amounts of labeled data available. However it is not always possible to have carefully labeled datasets. In this paper, we propose a weak supervision method that only uses class information rather than source signals for learning to separate short utterance mixtures. We associate a variational autoencoder (VAE) with each class within a non-negative model. We demonstrate that deep convolutional VAEs provide a prior model to identify complex signals in a sound mixture without having access to any source signal. We show that the separation results are on par with source signal supervision.
Article
Understanding Covid-19 Mobility Through Human Capital: A Unified Causal Framework
(Springer, 2024) Bilgel, Fırat; Karahasan, Burhan Can
Conference Object
Yüz Derinlik Imgelerinde Güdümlü İniş Yöntemini Kullanarak Nirengi Noktası Bulma
(Institute of Electrical and Electronics Engineers Inc., 2015) Akarun, Lale; Camgöz, Necati Cihan; Gökberk, Berk
Conference Object
Turkish Broadcast News Transcription Revisited
(Institute of Electrical and Electronics Engineers Inc., 2018) Arisoy, Ebru; Saraglar, Murat