Bilgisayar Mühendisliği Bölümü Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.11779/1940
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Conference Object Citation - Scopus: 7The Use of Neurometric and Biometric Research Methods in Understanding the User Experience During Product Search of First-Time Buyers in E-Commerce - Conference Paper(Springer, 2017) Rızvanoğlu, Kerem; Gürvardar, İrfan; Çakar, Tuna; Öztürk, Özgürol; Zengin Çelik, Deniz; Çelik, Deniz ZenginUnderstanding user experience (UX) during e-commerce has been a relatively important research area especially in the last decade. The use of conventional methods in UX such as task-observation, in-depth interviews and questionnaires has already contributed for the measurement of the efficiency and effectiveness. This empirical study has aimed to make use of both conventional and neuroscientific methods simultaneously to provide a richer analysis framework for understanding the product search experience of the first-time buyers. The current work provides insights for the results from the combined use of conventional and neuroscientific-biometric methods in a UX study. Although this has been an exploratory study within a limited literature, the obtained results indicate a potential use of these methods for UX research, which may contribute to improve the relevant experience in various digital platforms.Conference Object Citation - Scopus: 1Improved Bounds on the Moments of Guessing Cost(IEEE, 2022-06-26) Arslan, Suayb S.; Haytaoglu, ElifGuessing a random variable with finite or countably infinite support in which each selection leads to a positive cost value has recently been studied within the context of "guessing cost". In those studies, similar to standard guesswork, upper and lower bounds for the rho-th moment of guessing cost are described in terms of the known measure Renyi's entropy. In this study, we non-trivially improve the known bounds using previous techniques along with new notions such as balancing cost. We have demonstrated that the novel lower bound proposed in this work, achieves 5.84%, 18.47% higher values than that of the known lower bound for rho = 1 and rho = 5, respectively. As for the upper bound, the novel expression provides 10.93%, 5.54% lower values than that of the previously presented bounds for rho = 1 and rho = 5, respectively.Conference Object Citation - WoS: 2Citation - Scopus: 2Base Station-Assisted Cooperative Network Coding for Cellular Systems With Link Constraints(IEEE, 2022-06-26) Arslan, Suayb S.; Pourmandi, Massoud; Haytaoglu, ElifWe consider a novel distributed data storage/caching scenario in a cellular network, where multiple nodes may fail/depart simultaneously To meet reliability, we allow cooperative regeneration of lost nodes with the help of base stations allocated in a set of hierarchical layers1. Due to this layered structure, a symbol download from each base station has a different cost, while the link capacities between the nodes of the cellular system and the base stations are also constrained. Under such a setting, we formulate the fundamental trade-off with closed form expressions between repair bandwidth cost and the storage space per node. Particularly, the minimum storage as well as bandwidth cost points are formulated. Finally, we provide an explicit optimal code construction for the minimum storage regeneration point for a special set of system parameters.Article Citation - WoS: 1Citation - Scopus: 4Classification of Skin Lesion Images With Deep Learning Approaches(University of Latvia, 2022) Kulavuz, Bahadır; Ertuğrul, Berkay; Bakırman, Tolga; Çakar, Tuna; Doğan, Metehan; Bayram, Bülent; Bayram, BuketSkin cancer is one of the most dangerous cancer types in the world. Like any other cancer type, early detection is the key factor for the patient's recovery. Integration of artificial intelligence with medical image processing can aid to decrease misdiagnosis. The purpose of the article is to show that deep learning-based image classification can aid doctors in the healthcare field for better diagnosis of skin lesions. VGG16 and ResNet50 architectures were chosen to examine the effect of CNN networks on the classification of skin cancer types. For the implementation of these networks, the ISIC 2019 Challenge has been chosen due to the richness of data. As a result of the experiments, confusion matrices were obtained and it was observed that ResNet50 architecture achieved 91.23% accuracy and VGG16 architecture 83.89% accuracy. The study shows that deep learning methods can be sufficiently exploited for skin lesion image classification. © 2022 Baltic Journal of Modern Computing. All rights reserved.Article Citation - WoS: 1Citation - Scopus: 2Extracting, Computing, Coordination: What Does a Triphasic Erp Pattern Say About Language Processing?(Elsevier, 2021-11-25) Çakar, Tuna; Eken, Aykut; Cedden, GülayThe 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.Conference Object Citation - WoS: 4Citation - Scopus: 4Cost of Guessing: Applications To Data Repair(Institute of Electrical and Electronics Engineers Inc., 2020-06-01) Arslan, Şuayb Şefik; Haytaoğlu, ElifIn this paper, we introduce the notion of cost of guessing and provide an optimal strategy for guessing a random variable taking values on a finite set whereby each choice may be associated with a positive finite cost value. Moreover, we drive asymptotically tight upper and lower bounds on the moments of cost of guessing problem. Similar to previous studies on the standard guesswork, established bounds on moments quantify the accumulated cost of guesses required for correctly identifying the unknown choice and are expressed in terms of the Rényi's entropy. A new random variable is introduced to bridge between cost of guessing and the standard guesswork and establish the guessing cost exponent on the moments of the optimal guessing. Furthermore, these bounds are shown to serve quite useful for finding repair latency cost for distributed data storage in which sparse graph codes may be utilized.Conference Object Citation - WoS: 4Citation - Scopus: 5Private Minutia-Based Fingerprint Matching(Springer-Verlag Berlin, 2015) Sarıer, Neyire DenizIn this paper, we propose an efficient biometric authentication protocol for fingerprints particularly suited for the minutia-based representation. The novelty of the protocol is that we integrate the most efficient (linear complexity) private set intersection cardinality protocol of Cristofaro et al. and a suitable helper data system for biometrics in order to improve the accuracy of the system. We analyze the security of our scheme in the standard model based on well-exploited assumptions, considering malicious parties, which is essential to eliminate specific attacks on biometric authentication schemes designed for semi-honest adversaries only. Finally, the complexity is compared to the existing provably secure schemes for fingerprint matching, which shows that the new proposal outperforms them both in semi-honest and malicious security models.