Bilgisayar Mühendisliği Bölümü Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.11779/1940
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Article Citation - WoS: 3Citation - Scopus: 3A Benchmark Dataset for Turkish Data-To Generation(Elsevier, 2023-01-01) Demir, Şeniz; Öktem, SezaIn 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.Article 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: 3Citation - Scopus: 3Exact Construction of Bs-Assisted Mscr Codes With Link Constraints(IEEE Communications Letters, 2022-02-01) Arslan, Şuayb ŞefikIt is clear that 5G network resources would be consumed by heavy data traffic owing to increased mobility, slicing, and layered/distributed storage system architecture. The problem is elevated when multiple node failures are repaired to address service quality requirements. Typical approaches include individual or cooperative data regeneration to efficiently utilize the available bandwidth. It is observed that storage systems of 5G and beyond technologies shall have a multi–layer architecture in which base stations (BS) would be present. Moreover, communication with each layer would be subject to various communication costs and link constraints. Under limited BS assistance and cooperation, the trade-off between storage per node and communication bandwidth has been established. In this trade–off, two operating points, namely minimum storage, and bandwidth regeneration are particularly important. In this study, we first identify the optimal number of BS use at the minimum storage regeneration point. An explicit code construction is provided subsequently for the exact minimum storage regeneration whereby each layer may help the repair process subject to a communication link constraint.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: 13Citation - Scopus: 16Face Recognition With Patch-Based Local Walsh Transform(Elsevier, 2018-02-01) Uzun-Per, Meryem; Gökmen, MuhittinIn this paper, we present a novel dense local image representation method called Local Walsh Transform (LWT)by applying the well-known Walsh Transform (WT) to each pixel of an image. The LWT decomposes an image into multiple components, and produces LWT complex images by using the symmetrical relationship between them. Cascaded LWT (CLWT) is also a dense local image representation obtained by applying the LWT again to real and imaginary parts of LWT complex images. Applying the LWT once more to real and imaginary parts of LWT complex images increases the success rate especially on low resolution images. In order to combine the advantages of sparse and dense local image representations, we present Patch-based LWT (PLWT) and Patch-based CLWT (PCLWT) by applying the LWT and CLWT, respectively, to patches extracted around landmarks of multi-scaled face images. The extracted high dimensional features of the patches are reduced through the application of the Whitened Principal Component Analysis (WPCA). Experimental results show that both thePLWT and PCLWT are robust to illumination and expression changes, occlusion and low resolution. The state-of-the-art performance is achieved on the FERET and SCface databases, and the second best unsupervised category result is achieved on the LFW database.