Bilgisayar Mühendisliği Bölümü Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.11779/1940

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Citation - WoS: 3
Citation - Scopus: 2
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: 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: 13
Citation - Scopus: 21
Advancements in Distributed Ledger Technology for Internet of Things
(Elsevier, 2020) Jurdak, Raja; Arslan, Şuayb Şefik; Krishnamachari, Bhaskar; Jelitto, Jens
Internet of Things (IoT) is paving the way for different kinds of devices to be connected and properly communicated at a mass scale. However, conventional mechanisms used to sustain security and privacy cannot be directly applied to IoT whose topology is increasingly becoming decentralized. Distributed Ledger Technologies (DLT) on the other hand comprise varying forms of decentralized data structures that provide immutability through cryptographically linking blocks of data. To be able to build reliable, autonomous and trusted IoT platforms, DLT has the potential to provide security, privacy and decentralized operation while adhering to the limitations of IoT devices. The marriage of IoT and DLT technology is not very recent. In fact many projects have been focusing on this interesting combination to address the challenges of smart cities, smart grids, internet of everything and other decentralized applications, most based on blockchain structures. In this special issue, the focus is on the new and broader technical problems associated with the DLT-based security and backend platform solutions for IoT devices and applications.
Citation - WoS: 29
Citation - Scopus: 41
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: 20
Citation - Scopus: 28
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: 4
Citation - Scopus: 5
Designing restorative landscapes for students: A Kansei engineering approach enhanced by VR and EEG technologies
(Elsevier, 2024) Karaca, Elif; Çakar, Tuna; Karaca, Mehmet; Gul, Hasan Huseyin Mirac
This study explores the alignment of specific landscape features within school environments with the core elements of Attention Restoration Theory (ART) that includes Coherence, Fascination, Compatibility, and Being Away. Utilizing Kansei Engineering, this research integrates emotional analysis into landscape design by employing Virtual Reality (VR) and Electroencephalogram (EEG) technologies to record students' responses to different landscape simulations. Analytical techniques, including the Taguchi Method and Analysis of Variance (ANOVA), were applied to evaluate the data. The findings have revealed that students associate a sense of enclosure with a coherent landscape and openness with a fascinating landscape, the lawn's significance was also highlighted for coherent landscape. However, limited insights were gained regarding Compatibility and Being Away. The study advocates for diverse cognitive zones within school landscapes to promote mental restoration, emphasizing the need for varied design elements that cater to the elevated experience of students.
Citation - WoS: 1
Citation - Scopus: 2
Extracting, Computing, Coordination: What Does a Triphasic Erp Pattern Say About Language Processing?
(Elsevier, 2021) Çakar, Tuna; Eken, Aykut; Cedden, Gülay
The current study aims at contributing to the interpretation of the most prominent language-related ERP effects, N400 and P600, by investigating how neural responses to congruent and incongruent sentence endings vary, when the language processor processes the full array of the lexico-syntactic content in verbs with three affixes in canonical Turkish sentences. The ERP signals in response to three different violation conditions reveal a similar triphasic (P200/N400/P600) pattern resembling in topography and peak amplitude The P200 wave is interpreted as the extraction of meaning from written.form by generating a code which triggers the computation of neuronal ensembles in the distributed LTM (N400). The P600 potential reflects the widely distributed coordination process of activated neuronal patterns of semantic and morphosyntactic cues by connecting the generated subsets of these patterns and adapting them into the current context. It further can be deduced that these ERP components reflect cognitive rather than linguistic processes. © 2021 Informa UK Limited, trading as Taylor & Francis Group.
Citation - WoS: 13
Citation - Scopus: 16
Face Recognition With Patch-Based Local Walsh Transform
(Elsevier, 2018) Uzun-Per, Meryem; Gökmen, Muhittin
In 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.
Citation - WoS: 5
Citation - Scopus: 7
Founsure 1.0: an Erasure Code Library With Efficient Repair and Update Features
(Elsevier, 2021) Arslan, Şuayb Şefik
Founsure 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.
Citation - WoS: 6
Citation - Scopus: 12
Graph-Based Turkish Text Normalization and Its Impact on Noisy Text Processing
(Elsevier, 2022) Topçu, Berkay; Demir, Şeniz
User generated texts on the web are freely-available and lucrative sources of data for language technology researchers. Unfortunately, these texts are often dominated by informal writing styles and the language used in user generated content poses processing difficulties for natural language tools. Experienced performance drops and processing issues can be addressed either by adapting language tools to user generated content or by normalizing noisy texts before being processed. In this article, we propose a Turkish text normalizer that maps non-standard words to their appropriate standard forms using a graph-based methodology and a context-tailoring approach. Our normalizer benefits from both contextual and lexical similarities between normalization pairs as identified by a graph-based subnormalizer and a transformation-based subnormalizer. The performance of our normalizer is demonstrated on a tweet dataset in the most comprehensive intrinsic and extrinsic evaluations reported so far for Turkish. In this article, we present the first graph-based solution to Turkish text normalization with a novel context-tailoring approach, which advances the state-of-the-art results by outperforming other publicly available normalizers. For the first time in the literature, we measure the extent to which the accuracy of a Turkish language processing tool is affected by normalizing noisy texts before being processed. An analysis of these extrinsic evaluations that focus on more than one Turkish NLP task (i.e., part-of-speech tagger and dependency parser) reveals that Turkish language tools are not robust to noisy texts and a normalizer leads to remarkable performance improvements once used as a preprocessing tool in this morphologically-rich language.
Citation - Scopus: 1
On the Distribution of the Threshold Voltage in Multi-Level Cell Flash Memories
(Elsevier, 2019) Pusane, Ali E; Ashrafi, Reza A; Arslan, Şuayb Şefik
In Multi-Level Cell (MLC) memories, multiple bits of information are packed within the cell to enable higher capacity and lower cost of manufacturing compared to those of the single-level cell flash. However, because of heavy information packing, MLC memories suffer from several error sources including inter-cell interference, retention error, and random telegraph noise which make their lifetime shorter. Having so many error sources that are statistically hard to characterize makes it challenging to properly derive the underlying probability distribution of the sensed threshold voltage, which is vital for finding optimal decision rules to secure better detection performance and hence better lifetime. Although several recent works have already considered this problem, they mostly recourse to few loose assumptions that are far from being realistic. In this study, a more comprehensive/general analysis is conducted to derive the probability density function of the final sensed voltage, and through realistic simplifications, closed form expressions are presented. Extensive computer simulations corroborate the accuracy of the derived analytical expressions, and we think they shall be essential for accurately estimating the reliability and the overall lifetime of modern MLC memories.
Citation - Scopus: 3
The Use of Neurometric and Biometric Research Methods in Understanding the User Experience of First-Time Buyers in E-Commerce - Book Chapter 94
(Elsevier, 2018) Çakar, Tuna; Öztürk, Özgürol; Rızvanoğlu, Kerem; Çelik, Deniz Zengin
User experience (UX) research has attracted increasing attention especially in the last decade as the demand for online shopping has increased by 30.7% from 2014 to 2015 in Turkey. The traditional methods including surveys/questionnaires, think-aloud procedures, and in-depth interviews have contributed greatly for understanding the problems during the use of shopping internet sites. On the other hand, the use of neuroscientific methods, such as biometrics and neurometrics, has also grabbed attention with the exciting idea of providing an objective means of understanding cognitive and affective processes during the user experience during online shopping. Despite significant/strong limitations, many researchers are interested in exploring actively its potential use in the field.

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