Bilgisayar Mühendisliği Bölümü Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.11779/1940
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Article Citation - WoS: 4Citation - Scopus: 8Unraveling Neural Pathways of Political Engagement: Bridging Neuromarketing and Political Science for Understanding Voter Behavior and Political Leader Perception(Frontiers Media SA, 2023-12-21) Çakar, Tuna; Filiz, GözdePolitical neuromarketing is an interdisciplinary field that combines marketing, neuroscience, and psychology to understand voter behavior and political leader perception. This interdisciplinary field offers novel techniques to understand complex phenomena such as voter engagement, political leadership, and party branding. This study aims to understand the neural activation patterns of voters when they are exposed to political leaders using functional near-infrared spectroscopy (fNIRS) and machine learning methods. We recruited participants and recorded their brain activity using fNIRS when they were exposed to images of different political leaders. This neuroimaging method (fNIRS) reveals brain regions central to brand perception, including the dorsolateral prefrontal cortex (dlPFC), the dorsomedial prefrontal cortex (dmPFC), and the ventromedial prefrontal cortex (vmPFC). Machine learning methods were used to predict the participants' perceptions of leaders based on their brain activity. The study has identified the brain regions that are involved in processing political stimuli and making judgments about political leaders. Within this study, the best-performing machine learning model, LightGBM, achieved a highest accuracy score of 0.78, underscoring its efficacy in predicting voters' perceptions of political leaders based on the brain activity of the former. The findings from this study provide new insights into the neural basis of political decision-making and the development of effective political marketing campaigns while bridging neuromarketing, political science and machine learning, in turn enabling predictive insights into voter preferences and behaviorArticle What Is the Effective Resolution of the Retinal Image of a Distant Face?(Vision Sciences Society Annual Meeting Abstract, 2023-08-01) Arslan , Şuayb Şefik; Fux, Michal; Sinha, PawanWe consider the following question: What is the effective resolution of a face image projected on the retina, when the face is at a specified distance from the eye? Though simple to state, this is a surprisingly challenging issue to resolve. The mapping between viewing distance and effective resolution cannot be readily derived based on the contrast sensitivity, Snellen acuity, or even the packing density of photoreceptors in the fovea. With initial guidelines derived from theoretical considerations, images of varying resolution were presented across a range of viewing distances. For each distance, participants were required to perform an ‘odd one out’ task. This involved detecting the one that was different from the rest in a 2x2 grid, with image resolution being the only dimension of variation. As the experiment progressed, the viewing distance decreased monotonically, and participants were able to detect increasingly subtle resolution differences between the three standard images and the outlier. The collected data have allowed us to establish the upper/lower bounds on the effective available resolution for typical human vision as a function of viewing distance. Interestingly, we find that humans perform significantly better, particularly at short ranges, than what a theoretical model predicts based on projected image size, cone density, and foveal extent. Accordingly, we suggest that the non-uniform in-fovea density, as well as less sharp fall-off in the acuity density function outside the fovea, need to be integrated into future theoretical models to translate viewing distance to perceived image characteristics. A pragmatic benefit of the <distance : effective-resolution> mapping is that it enables a direct comparison of human face recognition performance as assessed across blur and viewing distance. Additionally, it allows us to systematically compare human performance on face recognition at varying distances with that of machine vision systems using the common axis of resolution.Article Comparing Humans and Deep Neural Networks on Face Recognition Under Various Distance and Rotation Viewing Conditions(Journal of Vision, 2023-08-01) Fux, Michal; Arslan , Şuayb Şefik; Jang, Hojin; Boix, Xavier; Cooper, Avi; Groth, Matt J; Sinha, PawanHumans possess impressive skills for recognizing faces even when the viewing conditions are challenging, such as long ranges, non-frontal regard, variable lighting, and atmospheric turbulence. We sought to characterize the effects of such viewing conditions on the face recognition performance of humans, and compared the results to those of DNNs. In an online verification task study, we used a 100 identity face database, with images captured at five different distances (2m, 5m, 300m, 650m and 1000m) three pitch values (00 - straight ahead, +/- 30 degrees) and three levels of yaw (00, 45, and 90 degrees). Participants were presented with 175 trials (5 distances x 7 yaw and pitch combinations, with 5 repetitions). Each trial included a query image, from a certain combination of range x yaw x pitch, and five options, all frontal short range (2m) faces. One was of the same identity as the query, and the rest were the most similar identities, chosen according to a DNN-derived similarity matrix. Participants ranked the top three most similar target images to the query image. The collected data reveal the functional relationship between human performance and multiple viewing parameters. Nine state-of-the-art pre-trained DNNs were tested for their face recognition performance on precisely the same stimulus set. Strikingly, DNN performance was significantly diminished by variations in ranges and rotated viewpoints. Even the best-performing network reported below 65% accuracy at the closest distance with a profile view of faces, with results dropping to near chance for longer ranges. The confusion matrices of DNNs were generally consistent across the networks, indicating systematic errors induced by viewing parameters. Taken together, these data not only help characterize human performance as a function of key ecologically important viewing parameters, but also enable a direct comparison of humans and DNNs in this parameter regimeArticle Citation - WoS: 6Citation - Scopus: 12During the Covid-19 Pandemic, Students' Opinions on Distance Education in Department of Engineering(International Association of Online Engineering (IAOE), 2022-03-15) Zaripova, Zülfiya F.; Karahoca, Dilek; Chikileva, Lyudmila S.; Lyalyaev, Sergey V.; Xu, Baoyun; Bayanova, Almira R.; Baoyun, XuThe decision regarding the distance education method in Turkey on March 15, 2020, has completely changed the learning and teaching methodology of all university students and educators, and it has been seen that all courses have started to be given with distance education. The purpose of this research is to examine the perspectives of engineering university students towards distance education during the Covid-19 pandemic. The research consists of engineering faculty students studying at various universities in the Aegean region and Russian Federation. In the research, a scanning model was used. The data of the research were collected from 520 engineering department university students from various universities in our country, according to the convenience sampling method, and through an online questionnaire filled out by the students. Thanks to this wide participation, results have been obtained that will explain the Covid-19 process related to distance education in a good way. In general, it has been concluded that students are happy to see them in distance education model courses, so they do not fall behind in their education, and university students watch their courses mostly with the help of smart devices.Article Citation - WoS: 2Citation - Scopus: 2Warning Notes in a Learner’s Dictionary: a Study of the Effectiveness of Different Formats(International Journal of Lexicography, 2022-01-25) Çakar, Tuna; Nesi, Hilary; Nural, ŞükrüThis study used an online correction task to explore the extent to which different types of warning notes in Longman Dictionary of Contemporary English Online were heeded when users tried to correct errors in the use of L2 target words. The task was completed by 332 participants, yielding 1,819 answers produced after clicking on links to relevant entries. Warning notes were categorised in terms of their formatting features, but there were found to be inconsistencies in the way the dictionary associated different categories with different kinds of learner error. Participants judged warning notes with more visual enhancements to be more useful, but in the correction task the position of the warning notes also seemed to affect the degree to which the warnings were successfully applied. Different types of warning notes in learners’ dictionaries have not been examined previously in any depth, and the results suggest that some adjustments to formatting and placement might make them more effective.Article Citation - WoS: 5Citation - Scopus: 7Founsure 1.0: an Erasure Code Library With Efficient Repair and Update Features(Elsevier, 2021-01-01) Arslan, Şuayb ŞefikFounsure is an open-source software library that implements a multi-dimensional graph-based erasure coding entirely based on fast exclusive OR (XOR) logic. Its implementation utilizes compiler optimizations and multi-threading to generate the right assembly code for the given multi-core CPU architecture with vector processing capabilities. Founsure possesses important features that shall find various applications in modern data storage, communication, and networked computer systems, in which the data needs protection against device, hardware, and node failures. As data size reached unprecedented levels, these systems have become hungry for network bandwidth, computational resources, and average consumed power. To address that, the proposed library provides a three-dimensional design space that trades off the computational complexity, coding overhead, and data/node repair bandwidth to meet different requirements of modern distributed data storage and processing systems. Founsure library enables efficient encoding, decoding, repairs/rebuilds, and updates while all the required data storage and computations are distributed across the network nodes.Article Citation - WoS: 30Citation - Scopus: 44An Efficient Framework for Visible-Infrared Cross Modality Person Re-Identification(Elsevier, 2020-09-01) 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.Article Citation - WoS: 28Citation - Scopus: 32Service-Aware Multi-Resource Allocation in Software-Defined Next Generation Cellular Networks(IEEE-Inst Electrical Electronics Engineers Inc, 2018) Arslan, Şuayb Şefik; Zeydan, Engin; Narmanloğlu, Ömer; Narmanlioglu, OmerNetwork slicing is one of the major solutions needed to meet the requirements of next generation cellular networks, under one common network infrastructure, in supporting multiple vertical services provided by mobile network operators. Network slicing makes one shared physical network infrastructure appear as multiple logically isolated virtual networks dedicated to different service types where each Network Slice (NS) benefits from on-demand allocated resources. Typically, the available resources distributed among NSs are correlated and one needs to allocate them judiciously in order to guarantee the service, MNO, and overall system qualities. In this paper, we consider a joint resource allocation strategy that weights the significance of the resources per a given NS by leveraging the correlation structure of different quality-of-service (QoS) requirements of the services. After defining the joint resource allocation problem including the correlation structure, we propose three novel scheduling mechanisms that allocate available network resources to the generated NSs based on different type of services with different QoS requirements. Performance of the proposed schedulers are then investigated through Monte-Carlo simulations and compared with each other as well as against a traditional max-min fairness algorithm benchmark. The results reveal that our schedulers, which have different complexities, outperform the benchmark traditional method in terms of service-based and overall satisfaction ratios, while achieving different fairness index levels.Article Citation - WoS: 9Citation - Scopus: 11An Efficient Multiscale Scheme Using Local Zernike Moments for Face Recognition(MDPI, 2018-05-21) Gökmen, Muhittin; Başaran, Emrah; Kamasak, Mustafa E.In this study, we propose a face recognition scheme using local Zernike moments (LZM), which can be used for both identification and verification. In this scheme, local patches around the landmarks are extracted from the complex components obtained by LZM transformation. Then, phase magnitude histograms are constructed within these patches to create descriptors for face images. An image pyramid is utilized to extract features at multiple scales, and the descriptors are constructed for each image in this pyramid. We used three different public datasets to examine the performance of the proposed method:Face Recognition Technology (FERET), Labeled Faces in the Wild (LFW), and Surveillance Cameras Face (SCface). The results revealed that the proposed method is robust against variations such as illumination, facial expression, and pose. Aside from this, it can be used for low-resolution face images acquired in uncontrolled environments or in the infrared spectrum. Experimental results show that our method outperforms state-of-the-art methods on FERET and SCface datasets.