Bilgisayar Mühendisliği Bölümü Koleksiyonu

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

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Now showing 1 - 14 of 14

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: 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: 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: 3
Citation - Scopus: 3
Array Bp-Xor Codes for Hierarchically Distributed Matrix Multiplication
(IEEE, 2021) Arslan, Şuayb Şefik
A novel fault-tolerant computation technique based on array Belief Propagation (BP)-decodable XOR (BP-XOR) codes is proposed for distributed matrix-matrix multiplication. The proposed scheme is shown to be configurable and suited for modern hierarchical compute architectures such as Graphical Processing Units (GPUs) equipped with multiple nodes, whereby each has many small independent processing units with increased core-to-core communications. The proposed scheme is shown to outperform a few of the well–known earlier strategies in terms of total end-to-end execution time while in presence of slow nodes, called stragglers. This performance advantage is due to the careful design of array codes which distributes the encoding operation over the cluster (slave) nodes at the expense of increased master-slave communication. An interesting trade-off between end-to-end latency and total communication cost is precisely described. In addition, to be able to address an identified problem of scaling stragglers, an asymptotic version of array BP-XOR codes based on projection geometry is proposed at the expense of some computation overhead. A thorough latency analysis is conducted for all schemes to demonstrate that the proposed scheme achieves order-optimal computation in both the sublinear as well as the linear regimes in the size of the computed product from an end-to-end delay perspective.
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: 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 - 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: 12
Citation - Scopus: 13
Enhanced Primordial Gravitational Waves From a Stiff Postinflationary Era Due To an Oscillating Inflaton
(Amer Physical Soc, 2024) Chen, Chao; Dimopoulos, Konstantinos; Eroncel, Cem; Ghoshal, Anish
We investigate two classes of inflationary models, which lead to a stiff period after inflation that boosts the signal of primordial gravitational waves (GWs). In both families of models studied, we consider an oscillating scalar condensate, which when far away from the minimum is overdamped by a warped kinetic term, a la alpha-attractors. This leads to successful inflation. The oscillating condensate is in danger of becoming fragmented by resonant effects when nonlinearities take over. Consequently, the stiff phase cannot be prolonged enough to enhance primordial GWs at frequencies observable in the near future for low orders of the envisaged scalar potential. However, this is not the case for a higher-order scalar potential. Indeed, we show that this case results in a boosted GW spectrum that overlaps with future observations without generating too much GW radiation to destabilize big bang nucleosynthesis. For example, taking alpha=O(1), we find that the GW signal can be safely enhanced up to Omega(GW) (f)similar to 10(-11) at frequency f similar to 10(2) Hz, which will be observable by the Einstein Telescope. Our mechanism ends up with a characteristic GW spectrum, which if observed, can lead to the determination of the inflation energy scale, the reheating temperature, and the shape (steepness) of the scalar potential around the minimum.
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: 52
Citation - Scopus: 66
An Investigation of the Neural Correlates of Purchase Behavior Through Fnirs
(2018) Cakir, Murat Perit; Yurdakul, Dicle; Girisken, Yener; Çakar, Tuna
Purpose This study aims to explore the plausibility of the functional near-infrared spectroscopy (fNIRS) methodology for neuromarketing applications and develop a neurophysiologically-informed model of purchasing behavior based on fNIRS measurements. Design/methodology/approach The oxygenation signals extracted from the purchase trials of each subject were temporally averaged to obtain average signals for buy and pass decisions. The obtained data were analyzed via both linear mixed models for each of the 16 optodes to explore their separate role in the purchasing decision process and a discriminant analysis to construct a classifier for buy/pass decisions based on oxygenation measures from multiple optodes. Findings Positive purchasing decisions significantly increase the neural activity through fronto-polar regions, which are closely related to OFC and vmPFC that modulate the computation of subjective values. The results showed that neural activations can be used to decode the buy or pass decisions with 85 per cent accuracy provided that sensitivity to the budget constraint is provided as an additional factor. Research limitations/implications The study shows that the fNIRS measures can provide useful biomarkers for improving the classification accuracy of purchasing tendencies and might be used as a main or complementary method together with traditional research methods in marketing. Future studies might focus on real-time purchasing processes in a more ecologically valid setting such as shopping in supermarkets. Originality/value This paper uses an emerging neuroimaging method in consumer neuroscience, namely, fNIRS. The decoding accuracy of the model is 85 per cent which presents an improvement over the accuracy levels reported in previous studies. The research also contributes to existing knowledge by providing insights in understanding individual differences and heterogeneity in consumer behavior through neural activities.
Citation - WoS: 3
Citation - Scopus: 5
Data Repair-Efficient Fault Tolerance for Cellular Networks Using Ldpc Codes
(IEEE, 2021) Haytaoglu, Elif; Kaya, Erdi; Arslan, Şuayb Şefik
The base station-mobile device communication traffic has dramatically increased recently due to mobile data, which in turn heavily overloaded the underlying infrastructure. To decrease Base Station (BS) interaction, intra-cell communication between local devices, known as Device-to-Device, is utilized for distributed data caching. Nevertheless, due to the continuous departure of existing nodes and the arrival of newcomers, the missing cached data may lead to permanent data loss. In this study, we propose and analyze a class of LDPC codes for distributed data caching in cellular networks. Contrary to traditional distributed storage, a novel repair algorithm for LDPC codes is proposed which is designed to exploit the minimal direct BS communication. To assess the versatility of LDPC codes and establish performance comparisons to classic coding techniques, novel theoretical and experimental evaluations are derived. Essentially, the theoretical/numerical results for repair bandwidth cost in presence of BS are presented in a distributed caching setting. Accordingly, when the gap between the cost of downloading a symbol from BS and from other local network nodes is not dramatically high, we demonstrate that LDPC codes can be considered as a viable fault-tolerance alternative in cellular systems with caching capabilities for both low and high code rates.
Citation - WoS: 3
Citation - Scopus: 5
On the Distribution Modeling of Heavy-Tailed Disk Failure Lifetime in Big Data Centers
(IEEE, 2021) Arslan, Şuayb Şefik; Zeydan, Engin
It has become commonplace to observe frequent multiple disk failures in big data centers in which thousands of drives operate simultaneously. Disks are typically protected by replication or erasure coding to guarantee a predetermined reliability. However, in order to optimize data protection, real life disk failure trends need to be modeled appropriately. The classical approach to modeling is to estimate the probability density function of failures using nonparametric estimation techniques such as kernel density estimation (KDE). However, these techniques are suboptimal in the absence of the true underlying density function. Moreover, insufficient data may lead to overfitting. In this article, we propose to use a set of transformations to the collected failure data for almost perfect regression in the transform domain. Then, by inverse transformation, we analytically estimated the failure density through the efficient computation of moment generating functions, and hence, the density functions. Moreover, we developed a visualization platform to extract useful statistical information such as model-based mean time to failure. Our results indicate that for other heavy-tailed data, the complex Gaussian hypergeometric distribution and classical KDE approach can perform best if the overfitting problem can be avoided and the complexity burden is overtaken. On the other hand, we show that the failure distribution exhibits less complex Argus-like distribution after performing the Box–Cox transformation up to appropriate scaling and shifting operations.
Citation - WoS: 9
Citation - Scopus: 10
Physicians’ Ethical Concerns About Artificial Intelligence in Medicine: a Qualitative Study: “the Final Decision Should Rest With a Human”
(Frontiers Media SA, 2024) Kahraman, F.; Aktas, A.; Bayrakceken, S.; Çakar, T.; Tarcan, H.S.; Bayram, B.; Ulman, Y.I.
Background/aim: Artificial Intelligence (AI) is the capability of computational systems to perform tasks that require human-like cognitive functions, such as reasoning, learning, and decision-making. Unlike human intelligence, AI does not involve sentience or consciousness but focuses on data processing, pattern recognition, and prediction through algorithms and learned experiences. In healthcare including neuroscience, AI is valuable for improving prevention, diagnosis, prognosis, and surveillance. Methods: This qualitative study aimed to investigate the acceptability of AI in Medicine (AIIM) and to elucidate any technical and scientific, as well as social and ethical issues involved. Twenty-five doctors from various specialties were carefully interviewed regarding their views, experience, knowledge, and attitude toward AI in healthcare. Results: Content analysis confirmed the key ethical principles involved: confidentiality, beneficence, and non-maleficence. Honesty was the least invoked principle. A thematic analysis established four salient topic areas, i.e., advantages, risks, restrictions, and precautions. Alongside the advantages, there were many limitations and risks. The study revealed a perceived need for precautions to be embedded in healthcare policies to counter the risks discussed. These precautions need to be multi-dimensional. Conclusion: The authors conclude that AI should be rationally guided, function transparently, and produce impartial results. It should assist human healthcare professionals collaboratively. This kind of AI will permit fairer, more innovative healthcare which benefits patients and society whilst preserving human dignity. It can foster accuracy and precision in medical practice and reduce the workload by assisting physicians during clinical tasks. AIIM that functions transparently and respects the public interest can be an inspiring scientific innovation for humanity. Copyright © 2024 Kahraman, Aktas, Bayrakceken, Çakar, Tarcan, Bayram, Durak and Ulman.
Citation - WoS: 11
Citation - Scopus: 17
Compress-Store on Blockchain: a Decentralized Data Processing and Immutable Storage for Multimedia Streaming
(Springer, 2022) Arslan, Şuayb Şefik; Turguy, Göker
Decentralization for data storage is a challenging problem for blockchain-based solutions as the blocksize plays a key role for scalability. In addition, specific requirements of multimedia data call for various changes in the blockchain technology internals. Considering one of the most popular applications of secure multimedia streaming, i.e., video surveillance, it is not clear how to judiciously encode incentivization, immutability, and compression into a viable ecosystem. In this study, we provide a genuine scheme that achieves this encoding for a video surveillance application. The proposed scheme provides a novel integration of data compression, immutable off-chain data storage using a new consensus protocol namely, Proof-of-WorkStore (PoWS) in order to enable fully useful work to be performed by the miner nodes of the network. The proposed idea is the first step towards achieving greener application of a blockchain-based environment to the video storage business that utilizes system resources efficiently.

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