05. Fakülteler

Permanent URI for this communityhttps://hdl.handle.net/20.500.11779/7

Browse

Now showing 1 - 20 of 197

Citation - WoS: 6
Citation - Scopus: 6
A 32-Society Investigation of the Influence of Perceived Economic Inequality on Social Class Stereotyping
(Wiley, 2022) Ashokkumar, Ashwini; Billet, Matthew; Becker, Maja; Peters, Kim; Jetten, Joland; Barry, Oumar; Tanjitpiyanond, Porntida; Peker, Müjde
There is a growing body of work suggesting that social class stereotypes are amplified when people perceive higher levels of economic inequality—that is, the wealthy are perceived as more competent and assertive and the poor as more incompetent and unassertive. The present study tested this prediction in 32 societies and also examines the role of wealth-based categorization in explaining this relationship. We found that people who perceived higher economic inequality were indeed more likely to consider wealth as a meaningful basis for categorization. Unexpectedly, however, higher levels of perceived inequality were associated with perceiving the wealthy as less competent and assertive and the poor as more competent and assertive. Unpacking this further, exploratory analyses showed that the observed tendency to stereotype the wealthy negatively only emerged in societies with lower social mobility and democracy and higher corruption. This points to the importance of understanding how socio-structural features that co-occur with economic inequality may shape perceptions of the wealthy and the poor. © 2022 The Authors. European Journal of Social Psychology published by John Wiley & Sons Ltd.
Citation - WoS: 2
Citation - Scopus: 1
A Benchmark Dataset for Turkish Data-To Generation
(Elsevier, 2022) Demir, Şeniz; Öktem, Seza
In the last decades, data-to-text (D2T) systems that directly learn from data have gained a lot of attention in natural language generation. These systems need data with high quality and large volume, but unfortunately some natural languages suffer from the lack of readily available generation datasets. This article describes our efforts to create a new Turkish dataset (Tr-D2T) that consists of meaning representation and reference sentence pairs without fine-grained word alignments. We utilize Turkish web resources and existing datasets in other languages for producing meaning representations and collect reference sentences by crowdsourcing native speakers. We particularly focus on the generation of single-sentence biographies and dining venue descriptions. In order to motivate future Turkish D2T studies, we present detailed benchmarking results of different sequence-to-sequence neural models trained on this dataset. To the best of our knowledge, this work is the first of its kind that provides preliminary findings and lessons learned from the creation of a new Turkish D2T dataset. Moreover, our work is the first extensive study that presents generation performances of transformer and recurrent neural network models from meaning representations in this morphologically-rich language.
Citation - WoS: 18
Citation - Scopus: 22
A Capacitated Lot Sizing Problem With Stochastic Setup Times and Overtime
(2019) Jabali, Ola; Gendreau, Michel; Jans, Raf; Taş, Duygu
In this paper, we study a Capacitated Lot Sizing Problem with Stochastic Setup Times and Overtime (CLSPSSTO). We describe a mathematical model that considers both regular costs (including production, setup and inventory holding costs) and expected overtime costs (related to the excess usage of capacity). The CLSP-SSTO is formulated as a two-stage stochastic programming problem. A procedure is proposed to exactly compute the expected overtime for a given setup and production plan when the setup times follow a Gamma distribution. A sample average approximation procedure is applied to obtain upper bounds and a statistical lower bound. This is then used to benchmark the performance of two additional heuristics. A first heuristic is based on changing the capacity in the deterministic counterpart, while the second heuristic artificially modifies the setup time. We conduct our computational experiments on well-known problem instances and provide comprehensive analyses to evaluate the performance of each heuristic. (C) 2018 Elsevier B.V. All rights reserved.
A Comparative Analysis of the Health Care Utilization and Costs of Patients Diagnosed With and Without Liver Cancer in the Us Medicare Population
(2017) Ogbomo, A.; Lin, Y.; Keshishian, A; Xie, L; Yuce, H; Başer, Onur
...
Citation - WoS: 5
Citation - Scopus: 8
A Data-Assisted Reliability Model for Carrier-Assisted Cold Data Storage Systems
(Elsevier, 2020) Arslan, Şuayb Şefik; Göker, Turguy; Peng, James
Cold data storage systems are used to allow long term digital preservation for institutions’ archive. The common functionality among cold and warm/hot data storage is that the data is stored on some physical medium for read-back at a later time. However in cold storage, write and read operations are not necessarily done in the same exact geographical location. Hence, a third party assistance is typically utilized to bring together the medium and the drive. On the other hand, the reliability modeling of such a decomposed system poses few challenges that do not necessarily exist in other warm/hot storage alternatives such as fault detection and absence of the carrier, all totaling up to the data unavailability issues. In this paper, we propose a generalized non-homogenous Markov model that encompasses the aging of the carriers in order to address the requirements of today's cold data storage systems in which the data is encoded and spread across multiple nodes for the long-term data retention. We have derived useful lower/upper bounds on the overall system availability. Furthermore, the collected field data is used to estimate parameters of a Weibull distribution to accurately predict the lifetime of the carriers in an example scale-out setting.
Citation - WoS: 1
Citation - Scopus: 3
A Meta-Analytic Review of the Association Between Theory of Mind and Aggression
(Elsevier, 2023) Imuta, Kana; Selçuk, Bilge; Yavuz-Müren, Melis; Turunç, Gamze; Ekerim-Akbulut, Müge
Although the association between theory of mind (ToM) and aggression has been theorized, empirical findings have not revealed a clear link between these constructs. In the current meta-analytic review, we integrated findings from 83 studies (141 effect sizes) involving 41,650 participants from 18 countries to elucidate the association between ToM and aggression in typically developing children, adolescents, and adults. We found a significant negative association between ToM and aggression overall (r = −0.15). Moreover, each type and function of aggression were negatively associated with Theory of Mind (ToM). Bullying—a distinct form of aggression—was not associated with ToM. The strength of the association between overall aggression and ToM varied as a function of methodological variables: First, studies that used self-report questionnaires to measure ToM and aggression yielded the strongest effect sizes, compared to those that used task-based assessments or questionnaires completed by others (parents, teachers, peers). Second, there was a difference in the ToM measurement with the measures examining ToM with non-false belief understanding tasks yielding a stronger mean effect than those that focused exclusively on false-belief understanding. Third, the magnitude of the negative association was found to increase with participants' age, though significant negative associations between ToM and aggression held across the lifespan. These results point to the critical link between ToM and aggressive tendencies and suggest the value in implementing interventions to improve mental state understanding across the age range to foster positive social interactions.
Citation - WoS: 9
Citation - Scopus: 12
A New Triangular Composite Shell Element With Damping Capability
(Elsevier, 2014) Körük, Hasan; Şanlıtürk, Kenan Yüce
This paper presents a new triangular composite shell element with damping capability. Formulation of the composite triangular shell element is based on stacking individual homogeneous triangular shell ele- ments on top of each other. The homogeneous shell element is an assembly of a triangular membrane element with drilling degrees of freedoms and a plate element. Damping capability is provided by means of complex element stiffness matrix of individual flat layers of the composite element. These elements with damping capability allow modelling general structures with damping treatments. A few test cases are modelled using triangular finite element developed here and the results of the complex eigenvalue analyses are compared with those of the quadrilateral shell elements proposed recently. The results obtained using the presented triangular and previous quadrilateral composite elements are also com- pared with those based on modal strain energy method and experimental results. Comparisons of the experimental and the theoretical results confirm that the modal properties including modal damping lev- els of structures with damping treatments can be predicted with high accuracy using the proposed finite element.
A Randomized Clinical Trial Comparing the Effects of Mindfulness-Based and Cognitive Behavioral Therapy-Based Stress Reduction in Medical Students
(Cambridge Univ Press, 2024) Pence, A. Yay; Coldur, M.; Atalay, Z.; Aslan, S.; Beba, B.; Sayin, C. Coskun; Ertek, I. Ekmekci
[No Abstract Available]
Citation - WoS: 6
Citation - Scopus: 7
A Reliability Model for Dependent and Distributed Mds Disk Array Units
(IEEE Transactions on Reliability, 2018) Arslan, Şuayb Şefik
Archiving and systematic backup of large digital data generates a quick demand for multi-petabyte scale storage systems. As drive capacities continue to grow beyond the few terabytes range to address the demands of today’s cloud, the likelihood of having multiple/simultaneous disk failures became a reality. Among the main factors causing catastrophic system failures, correlated disk failures and the network bandwidth are reported to be the two common source of performance degradation. The emerging trend is to use efficient/sophisticated erasure codes (EC) equipped with multiple parities and efficient repairs in order to meet the reliability/bandwidth requirements. It is known that mean time to failure and repair rates reported by the disk manufacturers cannot capture life-cycle patterns of distributed storage systems. In this study, we develop failure models based on generalized Markov chains that can accurately capture correlated performance degradations with multiparity protection schemes based on modern maximum distance separable EC. Furthermore, we use the proposed model in a distributed storage scenario to quantify two example use cases: Primarily, the common sense that adding more parity disks are only meaningful if we have a decent decorrelation between the failure domains of storage systems and the reliability of generic multiple single-dimensional EC protected storage systems.
Citation - WoS: 6
Citation - Scopus: 6
A Strong Integer Programming Formulation for Hybrid Flowshop Scheduling
(Taylor & Francis, 2019) Ağralı, Semra; Ünal, A. Tamer; Taşkın, Z. Caner
We consider a hybrid flowshop scheduling problem that includes parallel unrelated discrete machines or batch processing machines in different stages of a production system. The problem is motivated by a bottleneck process within the production system of a transformer producer located in the Netherlands. We develop an integer programming model that minimises the total tardiness of jobs over a finite planning horizon. Our model is applicable to a wide range of production systems organised as hybrid flowshops. We strengthen our integer program by exploiting the special properties of some constraints in our formulation. We develop a decision support system (DSS) based on our proposed optimisation model. We compare the results of our initial optimisation model with an improved formulation as well as with a heuristic that was in use at the company before the implementation of our DSS. Our results show that the improved optimisation model significantly outperforms the heuristic and the initial optimisation model in terms of both the solution time and the strength of its linear programming relaxation.
Citation - WoS: 23
Citation - Scopus: 25
Acoustic Particle Palpation for Measuring Tissue Elasticity
(American Institute of Physics, 2015) El Ghamrawy, Ahmed; Körük, Hasan; Choi, James J; Pouliopoulos, Antonios N
We propose acoustic particle palpation—the use of sound to press a population of acoustic particles against an interface—as a method for measuring the qualitative and quantitative mechanical properties of materials. We tested the feasibility of this method by emitting ultrasound pulses across a tunnel of an elastic material filled with microbubbles. Ultrasound stimulated the microbubble cloud to move in the direction of wave propagation, press against the distal surface, and cause deformations relevant for elasticity measurements. Shear waves propagated away from the palpation site with a velocity that was used to estimate the material’s Young’s modulus.
Citation - WoS: 28
Citation - Scopus: 41
An Analysis of Elementary School Children's Fractional Knowledge Depicted With Circle, Rectangle, and Number Line Representations
(Springer, 2015) Tunç-Pekkan, Zelha
It is now well known that fractions are difficult concepts to learn as well as to teach. Teachers usually use circular pies, rectangular shapes and number lines on the paper as teaching tools for fraction instruction. This article contributes to the field by investigating how the widely used three external graphical representations (i.e., circle, rectangle, number line) relate to students' fractional knowledge and vice versa. For understanding this situation, a test using three representations with the same fractional knowledge framed within Fractional Scheme Theory was developed. Six-hundred and fifty-six 4th and 5th grade US students took the test. A statistical analysis of six fractional Problem Types, each with three external graphical representations (a total of 18 problems) was conducted. The findings indicate that students showed similar performance in circle and rectangle items that required using part-whole fractional reasoning, but students' performance was significantly lower on the items with number line graphical representation across the Problem Types. In addition, regardless of the representation, their performance was lower on items requiring more advanced fractional thinking compared to part-whole reasoning. Possible reasons are discussed and suggestions for teaching fractions with graphical representations are presented. Copyright of Educational Studies in Mathematics is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.
Citation - WoS: 29
Citation - Scopus: 38
An Efficient Framework for Visible-Infrared Cross Modality Person Re-Identification
(Elsevier, 2020) Gökmen, Muhittin; Başaran, Emrah; Kamasak, Mustafa E.
Visible-infrared cross-modality person re-identification (VI-ReId) is an essential task for video surveillance in poorly illuminated or dark environments. Despite many recent studies on person re-identification in the visible domain (ReId), there are few studies dealing specifically with VI-ReId. Besides challenges that are common for both ReId and VI-ReId such as pose/illumination variations, background clutter and occlusion, VI-ReId has additional challenges as color information is not available in infrared images. As a result, the performance of VI-ReId systems is typically lower than that of ReId systems. In this work, we propose a four-stream framework to improve VI-ReId performance. We train a separate deep convolutional neural network in each stream using different representations of input images. We expect that different and complementary features can be learned from each stream. In our framework, grayscale and infrared input images are used to train the ResNet in the first stream. In the second stream, RGB and three-channel infrared images (created by repeating the infrared channel) are used. In the remaining two streams, we use local pattern maps as input images. These maps are generated utilizing local Zernike moments transformation. Local pattern maps are obtained from grayscale and infrared images in the third stream and from RGB and three-channel infrared images in the last stream. We improve the performance of the proposed framework by employing a re-ranking algorithm for post-processing. Our results indicate that the proposed framework outperforms current state-of-the-art with a large margin by improving Rank-1/mAP by 29.79%/30.91% on SYSU-MM01 dataset, and by 9.73%/16.36% on RegDB dataset.
Citation - WoS: 25
Citation - Scopus: 25
An Efficient Linear Programming Based Method for the Influence Maximization Problem in Social Networks
(Elsevier, 2019) Güney, Evren
The influence maximization problem (IMP) aims to determine the most influential individuals within a social network. In this study first we develop a binary integer program that approximates the original problem by Monte Carlo sampling. Next, to solve IMP efficiently, we propose a linear programming relaxation based method with a provable worst case bound that converges to the current state-of-the-art 1-1/e bound asymptotically. Experimental analysis indicate that the new method is superior to the state-of-the-art in terms of solution quality and this is one of the few studies that provides approximate optimal solutions for certain real life social networks.
An Efficient Linear Programming Based Method for the Influence Maximization Problem in Social Networks (vol 503, Pg 589, 2019)
(Elsevier, 2020) Güney, Evren
The influence maximization problem (IMP) aims to determine the most influential individuals within a social network. In this study first we develop a binary integer program thatapproximates the original problem by Monte Carlo sampling. Next, to solve IMP efficiently,we propose a linear programming relaxation based method with a provable worst casebound that converges to the current state-of-the-art 1 − 1/e bound asymptotically. Experimental analysis indicate that the new method is superior to the state-of-the-art in termsof solution quality and this is one of the few studies that provides approximate optimalsolutions for certain real life social networks.
Citation - WoS: 19
Citation - Scopus: 27
An Evaluation of Recent Neural Sequence Tagging Models in Turkish Named Entity Recognition
(Elsevier, 2021) Makaroğlu, Didem; Demir, Şeniz; Aras, Gizem; Çakır, Altan
Named entity recognition (NER) is an extensively studied task that extracts and classifies named entities in a text. NER is crucial not only in downstream language processing applications such as relation extraction and question answering but also in large scale big data operations such as real-time analysis of online digital media content. Recent research efforts on Turkish, a less studied language with morphologically rich nature, have demonstrated the effectiveness of neural architectures on well-formed texts and yielded state-of-the art results by formulating the task as a sequence tagging problem. In this work, we empirically investigate the use of recent neural architectures (Bidirectional long short-term memory (BiLSTM) and Transformer-based networks) proposed for Turkish NER tagging in the same setting. Our results demonstrate that transformer-based networks which can model long-range context overcome the limitations of BiLSTM networks where different input features at the character, subword, and word levels are utilized. We also propose a transformer-based network with a conditional random field (CRF) layer that leads to the state-of-the-art result (95.95% f-measure) on a common dataset. Our study contributes to the literature that quantifies the impact of transfer learning on processing morphologically rich languages.
Citation - WoS: 26
Citation - Scopus: 33
An Explanatory Sequential Mixed-Method Research on the Full-Scale Implementation of Flipped Learning in the First Years of the World's First Fully Flipped University: Departmental Differences
(Elsevier, 2021) Demir, Ömer; Birgili, Bengi
This study evaluates the first years of the full-scale flipped learning implementation process that began with an authority innovation-decision at the world's first fully flipped university in terms of departmental differences. The study employs an explanatory sequential mixed-method research. The primary respondents were 69 freshmen enrolled in the Faculty of Education at a private university in Istanbul, Turkey. In addition to student participants, five faculty members were recruited to the study. The primary data was collected through a Likert-type scale on flipped learning, including components on motivation, course structure, and interaction. Pre and post semi-structured interviews and a structured ranking form were also used to support the quantitative data. The findings of the study reveal that the students felt relatively unmotivated when instructed through flipped learning, although were satisfied with the course structure. In general, the students lacked student-student interaction. Due to the nature of the Guidance and Psychological Counseling department, the students faced some difficulties in engaging in all three types of interaction (student-student, student-educator, and student-content). Lengthy and poor-quality videos and students' lack of preparation for classes emerged as major problems in flipped learning. The faculty members complained about the amount of time required for pre-class preparation (i.e., recording flipped videos). This paper discusses how to foster motivation, collaboration, discussion, and interaction in flipped learning in higher education settings so as to guide prospective practitioners.
Citation - WoS: 4
Citation - Scopus: 6
An Online Laboratory School Research on Pre-Service Mathematics Teachers’ Experiences and Mathematics Teaching Anxiety
(Springer, 2022) Ölmez, İbrahim Burak; Taylan, Rukiye Didem; Pekkan, Tunç Zelha
During the COVID-19 pandemic, we founded an Online Laboratory School (OLS) under the roof of a university in Turkey to support students from public schools that were not technologically prepared for an online education and to provide an opportunity for our pre-service teachers (PSTs) to continue their internship by teaching online. The purpose of this research, consisting of two studies, was to examine experiences of 43 PSTs (first-, third- and fourth-years) during the OLS period of 8 weeks and how the OLS affected their mathematics teaching anxiety during Fall 2020. In the first study, we administered a survey to inquire into PSTs’ views on their experiences at the OLS, and in the second study we examined their mathematics teaching anxiety before and after the OLS experience using another survey. One main result was that the OLS experience served as an effective introduction to the profession for first-year PSTs and fourth- and third-year PSTs reported learning in-depth about online teaching in terms of the planning, teaching, and reflecting cycle. Another main result was that PSTs had mathematics teaching anxiety from “a little” to “a moderate amount” before the OLS and their teaching anxiety did not significantly change during the OLS period of 8 weeks. PSTs experienced highest mathematics teaching anxiety when they were observed and evaluated by supervisors during their teaching. The implications of these findings are discussed for teacher education programs.
Citation - WoS: 46
Citation - Scopus: 53
An Optimization Model for Carbon Capture & Storage/Utilization Vs. Carbon Trading: a Case Study of Fossil-Fired Power Plants in Turkey
(2018) Uctug, Fehmi Görkem; Ağralı, Semra; Türkmen, Burçin Atılgan
We consider fossil-fired power plants that operate in an environment where a cap and trade system is in operation. These plants need to choose between carbon capture and storage (CCS), carbon capture and utilization (CCU), or carbon trading in order to obey emissions limits enforced by the government. We develop a mixed-integer programming model that decides on the capacities of carbon capture units, if it is optimal to install them, the transportation network that needs to be built for transporting the carbon captured, and the locations of storage sites, if they are decided to be built. Main restrictions on the system are the minimum and maximum capacities of the different parts of the pipeline network, the amount of carbon that can be sold to companies for utilization, and the capacities on the storage sites. Under these restrictions, the model aims to minimize the net present value of the sum of the costs associated with installation and operation of the carbon capture unit and the transportation of carbon, the storage cost in case of CCS, the cost (or revenue) that results from the emissions trading system, and finally the negative revenue of selling the carbon to other entities for utilization. We implement the model on General Algebraic Modeling System (GAMS) by using data associated with two coal-fired power plants located in different regions of Turkey. We choose enhanced oil recovery (EOR) as the process in which carbon would be utilized. The results show that CCU is preferable to CCS as long as there is sufficient demand in the EOR market. The distance between the location of emission and location of utilization/storage, and the capacity limits on the pipes are an important factor in deciding between carbon capture and carbon trading. At carbon prices over $15/ton, carbon capture becomes preferable to carbon trading. These results show that as far as Turkey is concerned, CCU should be prioritized as a means of reducing nationwide carbon emissions in an environmentally and economically rewarding manner. The model developed in this study is generic, and it can be applied to any industry at any location, as long as the required inputs are available. (C) 2018 Elsevier Ltd. All rights reserved.
Citation - WoS: 4
Citation - Scopus: 4
Application of Ultrasonic Vibrations for Minimization of the Accumulation of Limescale in Steam Irons
(Elsevier, 2018) Körük, Hasan; Şanlıtürk, Kenan Yüce; Serenli, Muzaffer
The accumulation of limescale in steam irons can significantly reduce the ironing efficiency. It is this problem that inspired us to introduce ultrasonic vibrations to irons in order to minimize limescale accumulation. This study describes a methodology for designing, modelling and optimizing an iron fitted with an ultrasonic exciter in an attempt to minimize limescale accumulation. In our methodology, first, an experimental demonstration of the potential benefits of ultrasonic vibrations in steam irons was conducted, using two existing irons, one of which was equipped with an ultrasonic exciter. Having confirmed the benefits, an experimental iron was designed and then optimized to maximise ultrasonic vibrations using finite element analyses within a predefined frequency range. To validate the results of the finite element analyses, a prototype iron base was built, and forced vibrations of this prototype, at ultrasonic frequencies ranging from 35 to 40 kHz, were measured using a laser vibrometer. The results of the theoretical and experimental vibration analyses as well as the physical experiments on the steam irons indicate that it is possible for ultrasonic vibrations to be utilized in irons to minimize the accumulation of limescale.

Browse

Browsing 05. Fakülteler by Scopus Q "Q1"