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Publication Metadata only A chain-binomial model for pull and push-based information diffusion(IEEE, 2006) Department of Mathematics; Department of Computer Engineering; Çağlar, Mine; Özkasap, Öznur; Faculty Member; Faculty Member; Department of Mathematics; Department of Computer Engineering; College of Sciences; College of Engineering; 105131; 113507We compare pull and push-based epidemic paradigms for information diffusion in large scale networks. Key benefits of these approaches are that they are fully distributed, utilize local information only via pair-wise interactions, and provide eventual consistency, scalability and communication topology-independence, which make them suitable for peer-to-peer distributed systems. We develop a chain-Binomial epidemic probability model for these algorithms. Our main contribution is the exact computation of message delivery latency observed by each peer, which corresponds to a first passage time of the underlying Markov chain. Such an analytical tool facilitates the comparison of pull and push-based spread for different group sizes, initial number of infectious peers and fan-out values which are also accomplished in this study. Via our analytical stochastic model, we show that push-based approach is expected to facilitate faster information spread both for the whole group and as experienced by each member.Publication Metadata only A classification and performance comparison of mobility models for ad hoc networks(Springer-Verlag Berlin, 2006) N/A; Department of Computer Engineering; Atsan, Emre; Özkasap, Öznur; Master Student; Faculty Member; Department of Computer Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 113507In mobile ad hoc network research, simulation plays an important role in determining the network characteristics and measuring performance. On the other hand, unrealistic simulation conditions may be misleading, instead of being explanatory. For this reason, constructing simulation models closer to the real circumstances is very significant. Movement behavior of mobile entities is one of the most important concepts for the realistic simulation scenarios in mobile ad hoc networks. In this study, we first provide a survey and a new hybrid classification of existing mobility models in the literature. We implemented the random direction and boundless simulation area models on Scalable Wireless Ad Hoc Network Simulator (SWANS) and conducted simulations of Ad Hoc On-Demand Distance Vector (AODV) protocol for these as well as the random walk and random waypoint models. Our comparative results for the mobility models are discussed on a variety of simulation settings and parameters.Publication Metadata only A combined interval and floating-point reciprocal unit(IEEE, 2005) N/A; N/A; Department of Computer Engineering; Küçükkabak, Umut; Akkaş, Ahmet; Master Student; Faculty Member; Department of Computer Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/AInterval arithmetic is one technique for accurate and reliable computing. Among interval arithmetic operations, division is the most time consuming operation. This paper presents the design and implementation of a combined interval and floating-point reciprocal unit. To compute the reciprocal of an operand, an initial approximation is computed first and then iterated twice by Newton-Raphson iteration. The combined interval and floating-point reciprocal unit computes the reciprocal of a double precision floating-point number in eleven clock cycles and the reciprocal of an interval in twenty-one clock cycles. The unit is implemented in VHDL and synthesized to estimate the area and the worst case delay. Simulation results showed that the least significant bit of the floating-point result cannot be guaranteed to be same for all cases compared to the result based on an infinite precision. For interval reciprocal, however, the true result is contained in the result interval.Publication Metadata only A containerized proof-of-concept implementation of LightChain system(Ieee, 2020) N/A; N/A; Department of Computer Engineering; N/A; Department of Computer Engineering; Department of Computer Engineering; Hassanzadeh-Nazarabadi, Yahya; Nayal, Nazir; Hamdan, Shadi Sameh; Özkasap, Öznur; Küpçü, Alptekin; PhD Student; Faculty Member; Master Student; Faculty Member; Faculty Member; Department of Computer Engineering; Graduate School of Sciences and Engineering; College of Engineering; Graduate School of Sciences and Engineering; College of Engineering; College of Engineering; N/A; N/A; N/A; 113507; 168060LightChain is the first Distributed Hash Table (DHT)-based blockchain with a logarithmic asymptotic message and memory complexity. In this demo paper, we present the software architecture of our open-source implementation of LightChain, as well as a novel deployment scenario of the entire LightChain system on a single machine aiming at results reproducibility.Publication Metadata only A hybrid edge-assisted machine learning approach for detecting heart disease(Institute of Electrical and Electronics Engineers (IEEE), 2022) Otoum, Safa; N/A; Department of Computer Engineering; Hayyolalam, Vahideh; Özkasap, Öznur; PhD Student; Faculty Member; Department of Computer Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 113507Various resources are provided by cloud computing over the Internet, which enable plenty of applications to be employed to offer different services for industries. However, cloud computing due to the relying on a central server/datacenter has limitations such as high latency and response time, which are so crucial in real time applications like healthcare systems. To solve this, edge computing paradigm paves the way and provides pioneering solutions by moving the computational and storage resources closer to the end users. Edge computing by facilitating the realtime applications becomes more suitable for healthcare systems. This paper uses edge technology for detecting heart disease in patients utilizing a hybrid machine learning method. Although there exist some works in this area, there is still a need for improving the prediction accuracy. To this end, this paper proposes a metaheuristic-based feature selection method using Black Widow Optimization (BWO) algorithm, and then, applies different classifiers on the selected features. The experimental results show that AdaBoost classifier along with BWO-based feature selection by 90.11 % accuracy outperforms other experimental methods, such as KNN, SVM, DT, and RF.Publication Metadata only A new interface for affective state estimation and annotation from speech(Institute of Electrical and Electronics Engineers (IEEE), 2019) Fidan, Umut; Tomar, Deniz; Ozdil, P. Gizem; Department of Computer Engineering; Erzin, Engin; Faculty Member; Department of Computer Engineering; College of Engineering; 34503Emotion recognition from speech has been an important research area in the recent past. In this study, the purpose is to predict the emotion annotations better for robot/agent and hence to develop a more human-like interaction between human and robot/agent. In this context, the emotion annotations of human-human interactions, acoustic feature extraction and spectrogram images are carried on human-human dyadic conversations. In the first study, the statistical summary results are matched with the corresponding annotation and emotion recognition is achieved by using Support Vector Machines. In the second study, a sliding window of a certain size and overlapping intervals are matched with the corresponding annotation and the machine is trained by using Convolutional Neural Networks. Consequently, a user interface is designed to contain the works aforementioned. Also, the models obtained are tested with the databases JESTKOD and CreativeIT on this interface, and yield promising results for human-like robot/agents./ Öz:Konuşmadan duygu tanıma, yakın geçmişte önemli bir araştırma alanı olmuştur. Bu çalışmada hedef, robot/aracının duygu durum değerlerini daha iyi tahmin etmesi ve dolasıyla in- san ve robot/aracı arasında daha insansı bir etkileşim geliştirmektir. Bu bağlamda, insan-insan etkileşiminde etiketlenmiş duygu durum değerleri, istatistiksel akustik ve spektral imge öznite- ˘ likleri insan-insan ikili ileti¸simine uygulanmı¸stır. ˙Ilk çalı¸smada, istatistiksel akustik öznitelikler ile etiketlemeler eşleştirilmiş ve Destek Vektör Makineleri kullanılarak duygu durum kestirimi yapılmıştır. ikinci kısımda, belli pencere aralıklarına sahip, örtüşen ve kayan spektral imgeler duygu durum değerleriyle eşleştirilmiş ve üzerinde Evrişimsel Sinir Ağları kullanılarak duygu durum kestirimi yapılmıştır. Son olarak, bu iki kestirim yöntemini barındıran yeni bir kullanıcı arayüzü tasarlanmıştır. Ayrıca, elde edilen modeller bu arayüz üzerinde JESTKOD ve CreativeIT veritabanları ile test edilmiş ve insan benzeri robot/aracılar için umut verici sonuçlar vermiştir.Publication Metadata only Adaptive per-GOP bandwidth allocation for H.264 video transmission over differentiated services networks(Ieee, 2005) De Martin, JC; Department of Computer Engineering; N/A; Department of Electrical and Electronics Engineering; De Vito, Fabio; Yılmaz, Elif Merve; Tekalp, Ahmet Murat; Other; Researcher; Faculty Member; Department of Computer Engineering; Department of Electrical and Electronics Engineering; College of Engineering; Law School; College of Engineering; N/A; 267672; 26207While transmitting over differentiated services networks, in case of severe congestion also the most privileged classes may experience losses. In those cases, and especially in case of video transmission, protecting a higher fraction of traffic can have the effect of decreasing the quality, due to the overload of high-priority classes. We propose a method to compute, at source side, the allocation of video traffic over the available classes to ensure the lowest decoder-side distortion and provide traffic friendliness. To show this algorithm performance, the simple case of Poisson traffic with a bottleneck shared-buffer router is shown. The same approach can be extended to other traffic characteristics and router architectures.Publication Metadata only Affective burst detection from speech using Kernel-fusion dilated convolutional neural networks(IEEE, 2022) N/A; N/A; Department of Computer Engineering; Köprü, Berkay; Erzin, Engin; N/A; Faculty Member; Department of Computer Engineering; N/A; College of Engineering; N/A; 34503As speech interfaces are getting richer and widespread, speech emotion recognition promises more attractive applications. In the continuous emotion recognition (CER) problem, tracking changes across affective states is an essential and desired capability. Although CER studies widely use correlation metrics in evaluations, these metrics do not always capture all the high-intensity changes in the affective domain. In this paper, we define a novel affective burst detection problem to capture high-intensity changes of the affective attributes accurately. We formulate a two-class classification approach to isolate affective burst regions over the affective state contour for this problem. The proposed classifier is a kernel-fusion dilated convolutional neural network (KFDCNN) architecture driven by speech spectral features to segment the affective attribute contour into idle and burst sections. Experimental evaluations are performed on the RECOLA and CreativeIT datasets. The proposed KFDCNN outperforms baseline feedforward neural networks on both datasets.Publication Metadata only Analysis of checkpointing algorithms for primary-backup replication(Institute of Electrical and Electronics Engineers (IEEE), 2017) N/A; Department of Computer Engineering; Güler, Berkin; Özkasap, Öznur; Master Student; Faculty Member; Department of Computer Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 113507Replication is useful for supporting fault-tolerance, reliable and recovery oriented distributed systems. Popular application areas include databases, P2P systems, web services and Internet of Things. In this study, we propose utilizing the checkpointing concept for improving the efficiency of the well-known primary-backup replication protocol in distributed systems. We developed a software framework based on an in-memory replicated key-value store to evaluate various checkpointing algorithms. Using the framework over geographically distributed nodes of the PlanetLab platform, we performed extensive experiments and analysis with several different metrics, including blocking time, checkpointing time, checkpoint size and recovery time. Experimental scenarios consist of using the well-known benchmarking tool, YCSB, performing realistic read/update queries through exemplary workloads. Our findings indicate that incremental checkpointing combined with a periodic usage is the most efficient approach with having up to 30-times better system throughput and 50% decrease in average blocking times compared to traditional primary-backup replication and other checkpointing algorithms.Publication Metadata only ATM allocation using decision tree-based algorithms(Ieee, 2021) Yurdakul, Hazal Hasret; Kasikci, Kerem; Cagatay, Ilhan; Guven, Melih; Koras, Murat; Department of Computer Engineering; Department of Industrial Engineering; Akgün, Barış; Gönen, Mehmet; Faculty Member; Faculty Member; Department of Computer Engineering; Department of Industrial Engineering; College of Engineering; College of Engineering; 258784; 237468Automated teller machines (ATM's) make it possible for customers to fulfill their financial operations easily and reduces the workload of bank branches if they are placed in convenient locations. Banks need to have ATMs allocated in favorable locations regarding customer concerns. In this study, the ATM allocation problem is handled using decision tree-based algorithms. To solve the problem, a machine learning algorithm should learn the characteristics of each defined region and understand factors affecting the business performance. Therefore, a grid system is designed by dividing Turkey by imaginary lines. Imaginary lines constitute small grids passing through each one-thousandth of a latitude degree and one-thousandth of a longitude degree. For each grid rectangle, the characteristics of the customers living or wandering there, the point of interest locations around the area, and the existence of the competitors' ATMs are determined. Then, algorithms are trained and scored using decision tree-based algorithms. To decide suitable grid areas for installment, the business value is calculated for each grid. A heat map presenting the scores of the whole country is created for visualization purposes. The proposed framework can be used to better allocate ATMs all around in Turkey./Öz: Bankamatikler (ATM), banka müşterilerinin finansal işlemlerini kolayca gerçekleştirebilmelerine imkan sunar, uygun noktalara yerleştirildiklerinde de şubelerin işyükünün azalmasına yardımcı olurlar. Müşteri istekleri göz önünde bulundurulduğunda bankalar ATM’lerini kolay erişilebilir konumlara yerleş- tirmelidir. Bu çalışmada ATM yerleşimi problemi karar ağacı temelli algoritmalarla işlenmiştir. Bu sorunu çözmek amacıyla yapay ögrenme algoritması tanımlanacak her bölgenin karak- teristiğini ve iş performansını etkileyen faktörleri öğrenmelidir. Bu sebeple, Türkiye’yi hayali çizgilerle ayıran bir ızgara sistemi tasarlandı. Hayali çizgiler bir enlem derecesinin ve bir boylam derecesinin binde birlik parçalarından geçen çizgilerden oluşan küçük ızgaralar meydana getirir. Her bir ızgara dikdörtgeni için, burada yaşayan veya gezen müşterilerin karakteristigi, o alan ve alanın çevresindeki ilgi alanları ve rakip bankaların ATM’lerinin varlığı belirlendi. Sonrasında, karar ağacı temelli algoritmalar kullanarak eğitildi ve değerlendirildi. Yerleşime uygun alanlara karar vermek amacıyla, her ızgaranın iş değeri hesaplandı. Görselleştirme maksatlı bütün ülkenin değerlerini gösteren bir ısı haritası oluşturuldu. Artık, önerilen sistem bütün Türkiye’de ATM’leri daha iyi yerleştirmek için kullanılabilir.