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Permanent URI for this collectionhttps://hdl.handle.net/20.500.14288/3
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Publication Metadata only On the rate of convergence of a classifier based on a transformer encoder(IEEE-Inst Electrical Electronics Engineers Inc, 2022) Gurevych, Iryna; Kohler, Michael; Department of Computer Engineering; Şahin, Gözde Gül; Faculty Member; Department of Computer Engineering; College of Engineering; 366984Pattern recognition based on a high-dimensional predictor is considered. A classifier is defined which is based on a Transformer encoder. The rate of convergence of the misclassification probability of the classifier towards the optimal misclassification probability is analyzed. It is shown that this classifier is able to circumvent the curse of dimensionality provided the a posteriori probability satisfies a suitable hierarchical composition model. Furthermore, the difference between the Transformer classifiers theoretically analyzed in this paper and the ones used in practice today is illustrated by means of classification problems in natural language processing.Publication Metadata only Guest editorial special issue on toward securing Internet of Connected Vehicles (IoV) from virtual vehicle hijacking(Institute of Electrical and Electronics Engineers (IEEE), 2019) Cao, Yue; Kaiwartya, Omprakash; Song, Houbing; Lloret, Jaime; Ahmad, Naveed; Department of Electrical and Electronics Engineering; Ergen, Sinem Çöleri; Faculty Member; Department of Electrical and Electronics Engineering; College of Engineering; 7211N/APublication Metadata only A second-order adaptive network model for organizational learning and usage of mental models for a team of match officials(2022) Kuilboer, Sam; Sieraad, Wesley; van Ments, Laila; Treur, Jan; Department of Computer Engineering; Canbaloğlu, Gülay; Undergraduate Student; Department of Computer Engineering; College of Engineering; N/AThis paper describes a multi-level adaptive network model for mental processes making use of shared mental models in the context of organizational learning in team-related performances. The paper describes the value of using shared mental models to illustrate the concept of organizational learning, and factors that influence team performances by using the analogy of a team of match officials during a game of football and show their behavior in a simulation of the shared mental model. The paper discusses potential elaborations of the different studied concepts, as well as implications of the paper in the domain of teamwork and team performance, and in terms of organizational learning.Publication Metadata only Stochastic models for the coordinated production and shipment problem in a supply chain(Pergamon-Elsevier Science Ltd, 2013) N/A; Department of Industrial Engineering; N/A; Department of Industrial Engineering; Kaya, Onur; Kubalı, Deniz; Örmeci, Lerzan; Faculty Member; Master Student; Faculty Member; Department of Industrial Engineering; College of Sciences; Graduate School of Sciences and Engineering; College of Engineering; 28405; N/A; 32863In this study, we consider the coordination of transportation and production policies between a single supplier and a single retailer in a stochastic environment. The supplier controls the production, holds inventory and ships the products to the retailer to satisfy the external demand. We model the system as a Markov decision process, and show that the optimal production and transportation decisions are complex and non-monotonic. Therefore, we analyze two widely-used shipment policies in the industry as well, namely time-based and quantity-based shipment policies in addition to a hybrid time-and-quantity based shipment policy. We numerically compare the performances of these policies with respect to the optimal policy and analyze the effects of the parameters in the system.Publication Metadata only Challenges and applications of automated extraction of socio-political events from text (case 2021): workshop and shared task report(Association for Computational Linguistics (ACL), 2021) Tanev, Hristo; Zavarella, Vanni; Piskorski, Jakub; Yeniterzi, Reyyan; Villavicencio, Aline; Department of Sociology; Department of Sociology; N/A; Department of Computer Engineering; Hürriyetoğlu, Ali; Yörük, Erdem; Mutlu, Osman; Yüret, Deniz; Teaching Faculty; Faculty Member; PhD Student; Faculty Member; Department of Sociology; Department of Computer Engineering; College of Social Sciences and Humanities; College of Social Sciences and Humanities; Graduate School of Sciences and Engineering; College of Engineering; N/A; 28982; N/A; 179996This workshop is the fourth issue of a series of workshops on automatic extraction of sociopolitical events from news, organized by the Emerging Market Welfare Project, with the support of the Joint Research Centre of the European Commission and with contributions from many other prominent scholars in this field. The purpose of this series of workshops is to foster research and development of reliable, valid, robust, and practical solutions for automatically detecting descriptions of sociopolitical events, such as protests, riots, wars and armed conflicts, in text streams. This year workshop contributors make use of the state-of-the-art NLP technologies, such as Deep Learning, Word Embeddings and Transformers and cover a wide range of topics from text classification to news bias detection. Around 40 teams have registered and 15 teams contributed to three tasks that are i) multilingual protest news detection, ii) fine-grained classification of socio-political events, and iii) discovering Black Lives Matter protest events. The workshop also highlights two keynote and four invited talks about various aspects of creating event data sets and multi- and cross-lingual machine learning in few- and zero-shot settings.Publication Metadata only EASER: energy aware scalable and reactive replication protocol for MANETs(Springer, 2016) N/A; N/A; Department of Computer Engineering; Department of Computer Engineering; Azar, Saeed Nourizadeh; Karaağaçlı, Kaan; Özkasap, Öznur; PhD Student; Undergraduate Student; Faculty Member; Department of Computer Engineering; N/A; Graduate School of Sciences and Engineering; College of Engineering; College of Engineering; N/A; N/A; N/A; 113507Mobile ad hoc networks (MANETs) depend on the nodes' collaboration to communicate and transfer data, and scaling the network size up greatly increases the energy needed to transfer data among far away nodes. To preserve nodes' energy and increase the network lifetime, data replication protocols have been proposed, which mainly increase data availability by creating nearby local copies of required data. In this work, first we provide a review of energy aware data replication protocols in MANETs. Then, by considering nodes' energy consumption, we propose EASER: Energy Aware Scalable and rEactive data Replication protocol. Our simulation results and comparison with SCALAR, energy aware ZRP and AODV protocols show that EASER provides improved network lifetime and data accessibility as the network size scales up with considering node energy levels.Publication Metadata only Special section on the 2011 joint symposium on computational aesthetics (CAe), non-photorealistic animation and rendering (NPAR), and sketch-based interfaces and modeling (SBIM)(Pergamon-Elsevier Science Ltd, 2012) Isenberg, Tobias; Asente, Paul; Collomosse, John; Department of Computer Engineering; Sezgin, Tevfik Metin; Faculty Member; Department of Computer Engineering; College of Engineering; 18632N/APublication Metadata only Per-GOP bitrate adaptation for 11.264 compressed video sequences(Springer, 2006) De Martin, J.C.; Department of Computer Engineering; N/A; Department of Electrical and Electronics Engineering; Department of Electrical and Electronics Engineering; De Vito, Fabio; Özçelebi, Tanır; Civanlar, Mehmet Reha; Tekalp, Ahmet Murat; Other; PhD Student; Faculty Member; Faculty Member; Department of Computer Engineering; Department of Electrical and Electronics Engineering; College of Engineering; Graduate School of Sciences and Engineering; College of Engineering; College of Engineering; N/A; N/A; 16372; 26207In video transmission over packet data networks, it may be desirable to adapt the coding rate according to bandwidth availability. Classical approaches to rate adaptation are bitstream switching, requiring the storage of several pre-coded versions of a video, or layered (scalable) video coding, which has coding efficiency and/or complexity penalties. In this paper we propose a new GOP-level rate adaptation scheme for a single stream, variable target bitrate H.264 encoder; this allows each group of pictures (GOP) to be encoded at a specified bitrate. We first compare the performance of the standard H.264 rate control algorithm with the proposed one in the case of constant target bitrate. Then, we present results on how close the new technique can track a specified per-GOP target bitrate schedule. Results show that the proposed approach can obtain the desired target rates with less than 5% error.Publication Metadata only A new robust consistent hybrid finite-volume/particle method for solving the PDF model equations of turbulent reactive flows(Pergamon-Elsevier Science Ltd, 2014) Department of Mechanical Engineering; Sheikhsarmast, Reza Mokhtarpoor; Türkeri, Hasret; Muradoğlu, Metin; PhD Student; PhD Student; Faculty Member; Department of Mechanical Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; N/A; 46561A new robust hybrid finite-volume (FV)/particle method is developed for solving joint probability density function (JPDF) model equations of statistically stationary turbulent reacting flows. The method is designed to remedy the deficiencies of the hybrid algorithm developed by Muradoglu et al. (1999, 2001). The density-based FV solver in the original hybrid algorithm has been found to be excessively dissipative and yet not very robust. To remedy these deficiencies, a pressure-based PISO algorithm in the open source FV package, OpenFOAM, is used to solve the Favre-averaged mean mass and momentum equations while a particle-based Monte Carlo algorithm is employed to solve the fluctuating velocity-turbulence frequency-compositions JPDF transport equation. The mean density is computed as a particle field and passed to the FV method. Thus the redundancy of the density fields in the original hybrid method is removed making the new hybrid algorithm more consistent at the numerical solution level. The new hybrid algorithm is first applied to simulate non-swirling cold and reacting bluff-body flows. The convergence of the method is demonstrated. In contrast with the original hybrid method, the new hybrid algorithm is very robust with respect to grid refinement and achieves grid convergence without any unphysical vortex shedding in the cold bluff-body flow case. In addition, the results are found to be in good agreement with the earlier PDF calculations and also with the available experimental data. Finally the new hybrid algorithm is successfully applied to simulate the more complicated Sydney swirling bluff-body flame 'SM1'. The method is also very robust for this difficult test case and the results are in good agreement with the available experimental data. In all the cases, the PISO-FV solver is found to be highly resilient to the noise in the mean density field extracted from the particles.Publication Metadata only Research issues for privacy and security of electronic health services(Elsevier, 2017) Department of Computer Engineering; Department of Computer Engineering; Department of Computer Engineering; Yüksel, Buket; Küpçü, Alptekin; Özkasap, Öznur; Teaching Faculty; Faculty Member; Faculty Member; Department of Computer Engineering; College of Engineering; College of Engineering; College of Engineering; 326941; 168060; 113507With the prevalence of information and communication technologies, Electronic Health Services (EHS) are commonly used by patients, doctors, and other healthcare professionals to decrease healthcare costs and provide efficient healthcare processes. However, using EHS increases the concerns regarding security, privacy, and integrity of healthcare data. Several solutions have been proposed to address these issues in EHS. In this survey, we categorize and evaluate state-of-the-art electronic health system research based on their architecture, as well as services including access control, emergency access, sharing, searching, and anonymity methods by considering their cryptographic approaches. Our survey differs from previous EHS related surveys in being method-based such that the proposed services are classified based on their methods and compared with other solutions. We provide performance comparisons and state commonly used methods for each category. We also identify relevant open problems and provide future research directions.