WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/20.500.11779/256

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Now showing 1 - 20 of 117

Citation - WoS: 2
Citation - Scopus: 5
Compositional Neural Network Language Models for Agglutinative Languages
(2016) Saraçlar, Murat; Arısoy, Ebru
Continuous space language models (CSLMs) have been proven to be successful in speech recognition. With proper training of the word embeddings, words that are semantically or syntactically related are expected to be mapped to nearby locations in the continuous space. In agglutinative languages, words are made up of concatenation of stems and suffixes and, as a result, compositional modeling is important. However, when trained on word tokens, CSLMs do not explicitly consider this structure. In this paper, we explore compositional modeling of stems and suffixes in a long short-term memory neural network language model. Our proposed models jointly learn distributed representations for stems and endings (concatenation of suffixes) and predict the probability for stem and ending sequences. Experiments on the Turkish Broadcast news transcription task show that further gains on top of a state-of-theart stem-ending-based n-gram language model can be obtained with the proposed models.
Ethnic Appropriation of Folk Narratives and Architecture in the Post-Ottoman Balkans
(Trakya Univ Balkan Yerlesesi Enstituler Binasi, 2025) Sezgin, Ahmet
Folk narratives about a master builder who falls or flies from the structure he built, similar to the myth of Icarus, are widespread in the Balkans. One such narrative, involving the Selimiye Mosque in Edirne, was first recorded in Bulgaria at the end of the 19th century. This narrative became a focal point of transnational debate between Turkish and Bulgarian nationalist rhetoric during a period of interstate tension in Thrace in the 1930s and 1940s. It intersected with the appropriation of Ottoman architectural heritage and the formation of national identity within a transnational context during the first half of the 20th century. After revealing the diversity of these folk narratives, this article explores how nationalist movements engage with modern reinterpretations of these narratives in the context of Ottoman architectural appropriation. While exploring this debate, the article highlights the tension between the syncretism of the narratives and the processes of national identity formation.
Citation - WoS: 8
Citation - Scopus: 5
Recognizing Non-Manual Signs in Turkish Sign Language
(IEEE, 2019) Gökberk, Berk; Akarun, Lale; Aktaş, Müjde
Recognition of non-manual components in sign language has been a neglected topic, partly due to the absence of annotated non-manual sign datasets. We have collected a dataset of videos with non-manual signs, displaying facial expressions and head movements and prepared frame-level annotations. In this paper, we present the Turkish Sign Language (TSL) non-manual signs dataset and provide a baseline system for non-manual sign recognition. A deep learning based recognition system is proposed, in which the pre-trained ResNet Convolutional Neural Network (CNN) is employed to recognize question, negation side to side and negation up-down, affirmation and pain movements and expressions. Our subject independent method achieves 78.49% overall frame-level accuracy on 483 TSL videos performed by six subjects, who are native TSL signers. Prediction results of consecutive frames are filtered for analyzing the qualitative results.
Energy Consumption at Home: Insights for Sustainable Smart Home Marketing
(Springer International Publishing Ag, 2023) Köse, Şirin Gizem; Cizer, Ece Ozer; Kazancoglu, Ipek
Energy consumption has been a vital subject for both energy producers and consumers. The intersection of energy consumption and home words are of increasing importance in both literature and practice. Households try to utilize energy in the most efficient and sustainable way. On the other hand, smart home technologies which let the households control their houses are on the rise. Those technologies also help balance the energy consumption and live in a more sustainable way. This study aims to underline the importance of smart home technologies to increase energy efficiency and pave the way for a more sustainable energy management. In line with this purpose, a bibliometric study has been conducted to enlighten the literature development in energy consumption and home subjects. The results are expected to be helpful for both literature and practice as well as energy providers and consumers.
Citation - Scopus: 4
Multi-View Reconstruction of 3d Human Pose With Procrustes Analysis
(IEEE, 2019) Gökberk, Berk; Akarun, Lale; Temiz, Hüseyin
Recovery of 3D human pose from cameras has been the subject of intensive research in the last decade. Algorithms that can estimate the 3D pose from a single image have been developed. At the same time, many camera environments have an array of cameras. In this paper, after aligning the poses obtained from single images using Procrustes Analysis, median filtering is utilized to eliminate outliers to find final reconstructed 3D body joint coordinates. Experiments performed on the CMU Panoptic, and Human3.6M databases demonstrate that the proposed system achieves accurate 3D body joint reconstructions. Additionally, we observe that camera selection is useful to decrease the system complexity while attaining the same level of reconstruction performance.
Dog Walker Segmentation
(IEEE, 2022) Ercan, Alperen; Karan, Baris; Çakar, Tuna
In this study dog walkers were separated into clusters according to walkers' walk habits. Due to the fact that the distributions were non-normal, normalization algorithms were applied before the onset of clustering. After normalizing, K Means algorithm and Gaussian Mixture Models used for finding optimum cluster count. According to these clusters, walkers' consecutive months separated to follow-up their behavioral traits. This part of the study adds value to the project to examine walkers' behaviors closer.
A Practical PCB-Based Framework for Spiking Neural Networks with a Half-Adder Example
(IEEE, 2025) Cikikci, Sevde Vuslat; Orek, Eren; Aysoy, Ayhan; Ozgen, Ali Kagan; Yavuz, Arda; Ayhan, Tuba
This paper addresses the half-adder problem using Spiking Neural Networks (SNNs). In a previous study, the XOR operation was successfully realized on a breadboard and in this study it is integrated into the half-adder structure. The system uses input signals at frequencies of 50 Hz and 100 Hz and the neurons are generated by the Leaky Integrate and Fire (LIF) model. Unlike other neuron models, the LIF model is less complex. In addition, it was preferred because of its biological meaningfulness compared to the Integrate and Fire model. The network, consisting of 18 neurons in total, shows that basic arithmetic operations can be performed with SNN. Overall, this study demonstrates that basic logic operations can be implemented in neural networks, thus providing new perspectives for digital calculation. The successful solution of the Half Adder problem using SNNs not only proves the calculation capabilities of SNNs, but also opens new perspectives for the development of more complex logical circuits using these biologically inspired neural circuits.
Citation - WoS: 536
Citation - Scopus: 652
Human Semantic Parsing for Person Re-Identification
(2018) Kalayeh, Mahdi M; Başaran, Emrah; Shah, Mubarak; Kamasak, Mustafa E; Gökmen, Muhittin
Person re-identification is a challenging task mainly dueto factors such as background clutter, pose, illuminationand camera point of view variations. These elements hinder the process of extracting robust and discriminative representations, hence preventing different identities from being successfully distinguished. To improve the representation learning, usually local features from human body partsare extracted. However, the common practice for such aprocess has been based on bounding box part detection.In this paper, we propose to adopt human semantic parsing which, due to its pixel-level accuracy and capabilityof modeling arbitrary contours, is naturally a better alternative. Our proposed SPReID integrates human semanticparsing in person re-identification and not only considerably outperforms its counter baseline, but achieves stateof-the-art performance. We also show that, by employinga simple yet effective training strategy, standard populardeep convolutional architectures such as Inception-V3 andResNet-152, with no modification, while operating solelyon full image, can dramatically outperform current stateof-the-art. Our proposed methods improve state-of-the-artperson re-identification on: Market-1501 [48] by ~17% inmAP and ~6% in rank-1, CUHK03 [24] by ~4% in rank-1and DukeMTMC-reID [50] by ~24% in mAP and ~10% inrank-1.
Supporting Flipped Learning: Digital Pedagogy, Training, and Resources
(Emerald Group Publishing Ltd., 2016) Kurban, Caroline Fell; Şahin, Muhammed
…
Citation - Scopus: 2
A Visualization Platfom for Disk Failure Analysis
(IEEE, 2018) Arslan, Şuayb Şefik; Yiğit, İbrahim Onuralp; Zeydan, Engin
It has become a norm rather than an exception to observe multiple disks malfunctioning or whole disk failures in places like big data centers where thousands of drives operate simultaneously. Data that resides on these devices is typically protected by replication or erasure coding for long-term durable storage. However, to be able to optimize data protection methods, real life disk failure trends need to be modeled. Modelling helps us build insights while in the design phase and properly optimize protection methods for a given application. In this study, we developed a visualization platform in light of disk failure data provided by BackBlaze, and extracted useful statistical information such as failure rate and model-based time to failure distributions. Finally, simple modeling is performed for disk failure predictions to alarm and take necessary system-wide precautions.
Evaluating a Model From Two Perspectives: Teachers and University Scholars
(2016) Aktekin, Nafiye Çiğdem
Productive strategies for evaluating outcomes are becoming increasingly important for the improvement of teacher education [1] and for any model that claims to offer the best practice. The University within School (UwS) model suggests that teacher education must be done through collaboration between universities and schools. According to Ozcan [2], the model combines two models of professional education: one is the traditional "apprenticeship-journeyman-master's" model, which is practice-based; the other is modern professional education, which is mostly based on theoretical knowledge and is implemented through formal schooling. Students and academicians of an education faculty of a foundation university have been practicing to this end in a workplace for two years. Teachers of the partner school have been part of this model as mentor teachers, role models, and participants. This study aims to evaluate the model from the perspectives of teachers and college faculty since the model aims to prepare the conceptual framework of teacher education through the participation of all partners. The perceptions of two faculty members and three teachers were investigated through interviews. The evaluation of the model will help to design the model more effectively.
Introduction To the Papers of Twg19: Mathematics Teachers and Classroom Practices
(Dublin City Univ Glasnevin Campus, 2017) Mosvold, Reidar; Skott, Jeppe; Taylan, Rukiye Didem; Drageset, Ove Gunnar; Sakonidis, Charalampos
…
The Flipped Approach To Higher Education Designing Universities for Today's Knowledge Economies and Societies Preface
(Emerald Group Publishing Ltd, 2016) Sahin, Muhammed; Kurban, Caroline Fell
Strategic Financial Management for Small and Medium Sized Companies Conclusion
(Emerald Group Publishing Ltd., 2015) Karadağ, Hande
...
Citation - WoS: 5
Citation - Scopus: 7
Cloud2hdd: Large-Scale Hdd Data Analysis on Cloud for Cloud Datacenters
(IEEE, 2020) Zeydan, Engin; Arslan, Şefik Şuayb
The main focus of this paper is to develop a distributed large scale data analysis platform for the opensource data of Backblaze cloud datacenter which consists of operational hard disk drive (HDD) information collected over an observable period of 2272 days (over 74 months). To carefully analyze the intrinsic characteristics of the hard disk behavior, we have exploited a large bolume of data and the benefits of Hadoop ecosystem as our big data processing engine. In other words, we have utilized a special distributed scheme on cloud for cloud HDD data, which is termed as Cloud2HDD. To classify the remaining lifetime of hard disk drives based on health indicators such as in-built S.M.A.R.T (Self-Monitoring, Analysis, and Reporting Technology) features, we used some of the state-of-the-art classification algorithms and compared their accuracy, precision, and recall rates simultaneously. In addition, importance of various S.M.A.R.T. features in predicting the true remaining lifetime of HDDs are identified. For instance, our analysis results indicate that Random Forest Classifier (RFC) can yield up to 94% accuracy with the highest precision and recall at a reasonable time by classifying the remaining lifetime of drives into one of three different classes, namely critical, high and low ideal states in comparison to other classification approaches based on a specific subset of S.M.A.R.T. features.
Citation - WoS: 15
Citation - Scopus: 40
An Overview of Blockchain Technologies: Principles, Opportunities and Challenges
(IEEE, 2018) Arslan, Şuayb Şefik; Mermer, Gültekin Berahan; Zeydan, Engin
Blokzincir, toplumumuzun birbiriyle iletişim kurma ve ticaret yapma biçiminde devrim yapma potansiyeline sahip, yakın zamanda ortaya çıkmış olan bir teknolojidir. Bu teknolojinin sağladığı en önemli avantaj aracı gerektiren bir oluşumda güvenilir bir merkezi kuruma ihtiyaç duymadan değer taşıyan işlemleri değiş tokuş edebilmesidir. Ayrıca, veri bütünlüğü, dahili orijinallik ve kullanıcı şeffaflığı sağlayabilir. Blokzincir, birçok yenilikçi uygulamanın temel alınacağı yeni internet olarak görülebilir. Bu çalışmada, genel çalışma prensibi, oluşan fırsatlar ve ileride karşılaşılabilecek zorlukları içerecek şekilde güncel blokzincir teknolojilerinin genel bir görünümünü sunmaktayız.
The Application of Two Bayesian Personalized Ranking Approaches Based on Item Recommendation From Implicit Feedback
(Ieee, 2024) Tagtekin, Burak; Sahin, Zeynep; Çakar, Tuna; Drias, Yassine
The present study has aimed to provide a different ranking approach that will be used actively in a sector-specific application regarding the optimization of item ranking presented to the users. The current online approach in several different applications still holds a manual ranking algorithm whose parameters are determined by the data specialists with adequate domain-knowledge. The obtained findings from the present study indicate that the optimized Bayesian Personalized Ranking models will be used for providing a suitable, data-driven input for the ranking system that would serve to be personalized. The outcomes of the present study also demonstrate that the model using LearnBPR optimized with a stochastic gradient descent algorithm outperform the other similar methods. The sample model outputs were also investigated by a user sample to ensure that the algorithm was working correctly. The next potential step is to provide a normalization process to include the extracted information to the current ranking system and observe the performance of this new algorithm with the A/B tests conducted.
Citation - WoS: 1
Citation - Scopus: 1
Adaptive Boosting of Dnn Ensembles for Brain-Computer Interface Spellers
(IEEE, 2021) Çatak, Yiğit; Aksoy, Can; Özkan, Hüseyin; Güney, Osman Berke; Koç, Emirhan; Arslan, Şuayb Şefik
Steady-state visual evoked potentials (SSVEP) are commonly used in brain computer interface (BCI) applications such as spelling systems, due to their advantages over other paradigms. In this study, we develop a method for SSVEP-based BCI speller systems, using a known deep neural network (DNN), which includes transfer and ensemble learning techniques. We test performance of our method on publicly available benchmark and BETA datasets with leave-one-subject-out procedure. Our method consists of two stages. In the first stage, a global DNN is trained using data from all subjects except one subject that is excluded for testing. In the second stage, the global model is fine-tuned to each subject whose data are used in the training. Combining the responses of trained DNNs with different weights for each test subject, rather than an equal weight, provide better performance as brain signals may differ significantly between individuals. To this end, weights of DNNs are learnt with SAMME algorithm with using data belonging to the test subject. Our method significantly outperforms canonical correlation analysis (CCA) and filter bank canonical correlation analysis (FBCCA) methods.
Citation - WoS: 31
Financial Innovation-Crowdfunding: Friend or Foe?
(Elsevier, 2015) Son-Turan, Semen
A phenomenon with a considerable past, and with new conspicuous investment models and financial products and servicesproliferated through the Internet; financial innovation seems to be almost ubiquitous these days. While there are numerousadvantages, especially nowadays through the exploitation of easily accessible, low cost and convenient e-commerce platforms,innovation in the finance sector does not come without its perils. Banks and traditional financial institutions are losing chunks ofmarket share to virtual intermediaries and investors are operating in relatively less regulated and, consequently, less secureenvironments. Furthermore, from the perspective of all stakeholders, there is a Knightian uncertainty component of the long-termramifications in investing in and through newly developed products and platforms. As such, it is only recently that economichistory witnessed the outbreak of the sub-prime mortgage crisis caused by the unraveling of a chain of events interlinked throughthe imprudent use of “innovative” derivative transactions involving credit default swaps backed by the insatiable appetite of the“irrationally exuberant” investor and the easement of regulation paving the leeway for predatory lending. This paper investigateswhether and to what extent innovative investment models such as crowdfunding, as the game-changer, forcing the tightlyregulated securities markets to adapt to the rules of the WEB 3.0 era and relieved through the provision, Title III, of the JOBSAct, could be a potential peril. To that end, it discusses the evolution of the equity crowdfunding model in the realm of thetechnology push - demand pull framework and analyzes the current situation of the market.
The Tuned Mass Damper as a Subject in Engineering Mechanics Dynamics
(IEEE, 2022) Dorantes-Gonzalez, Dante Jorge
The course of Engineering Mechanics Dynamics is one of the most challenging courses for both mechanical and civil engineering programs, among others. But few universities dare to introduce projects to enhance students' curiosity, interest, and engagement toward engineering by constructing do-it-yourself physical prototypes, making measurements, and calculations to compete for the best performance. The intention of this project is to introduce a complex multiple-degree-of-freedom vibration problem in an easy manner, namely, the topic of a tuned mass damper (TMD) applied to earthquake-like oscillations. This type of projects directly addresses all seven student outcomes recommended by the Accreditation Board of Engineering and Technology (ABET). The project develops critical thinking and inquiry skills by designing and constructing the prototype of a building-like structure and its corresponding TMD; conducting an experiment under certain constraints to test the attenuation after an initial displacement; applying an open-source freeware to plot and measure underdamped oscillations; calculating main vibration parameters; as well as comparing performance results with another teams. Students approach this complex tunning problem by trial-and-error of key TMD parameters, a strategy that sparks fun and gambling to the process and competition for the best performance in attenuation efficiency. Data from direct observation of students' performance, student surveys, reports, presentation videos, office hours, and interviews showed that students enthusiastically responded at all project stages, understood the TMD functioning, and appreciated the value of dynamics in engineering in a more meaningful way than it would be without this type of projects.

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