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Publication Metadata only 3D progressive compression with octree particles(Akademische Verlagsgesellsch Aka Gmbh, 2002) Schmitt, Francis; Department of Computer Engineering; N/A; Yemez, Yücel; Faculty Member; Department of Computer Engineering; College of Engineering; N/A; 107907; N/AThis paper improves the storage efficiency of the progressive particle-based modeling scheme presented in [14, 15] by using entropy coding techniques. This scheme encodes the surface geometry and attributes in terms of appropriately ordered oc-tree particles, which can then progressively be decoded and rendered by the-viewer by means of a fast direct triangulation technique. With the introduced entropy coding technique, the bitload of the multi-level representation for geometry encoding reduces to 9-14 bits per particle (or 4.5-7 bits per triangle) for 12-bit quantized geometry.Publication Metadata only 3D shape correspondence by isometry-driven greedy optimization(IEEE Computer Soc, 2010) N/A; Department of Computer Engineering; Sahillioğlu, Yusuf; Yemez, Yücel; PhD Student; Faculty Member; Department of Computer Engineering; Graduate School of Sciences and Engineering; College of Engineering; 215195; 107907We present an automatic method that establishes 3D correspondence between isometric shapes. Our goal is to find an optimal correspondence between two given (nearly) isometric shapes, that minimizes the amount of deviation from isometry. We cast the problem as a complete surface correspondence problem. Our method first divides the given shapes to be matched into surface patches of equal area and then seeks for a mapping between the patch centers which we refer to as base vertices. Hence the correspondence is established in a fast and robust manner at a relatively coarse level as imposed by the patch radius. We optimize the isometry cost in two steps. in the first step, the base vertices are transformed into spectral domain based on geodesic affinity, where the isometry errors are minimized in polynomial time by complete bipartite graph matching. the resulting correspondence serves as a good initialization for the second step of optimization in which we explicitly minimize the isometry cost via an iterative greedy algorithm in the original 3D Euclidean space. We demonstrate the performance of our method on various isometric (or nearly isometric) pairs of shapes for some of which the ground-truth correspondence is available.Publication Metadata only A classification of concurrency bugs in java benchmarks by developer intent(Association for Computing Machinery (ACM), 2006) Department of Computer Engineering; Department of Computer Engineering; N/A; Keremoğlu, M. Erkan; Taşıran, Serdar; Elmas, Tayfun; Researcher; Faculty Member; PhD Student; Department of Computer Engineering; College of Engineering; College of Engineering; Graduate School of Sciences and Engineering; N/A; N/A; N/AThis work addresses the issue of selecting the formal correctness criterion for a concurrent Java program that best corresponds to the developer's intent. We study a set of concurrency-related bugs detected in Java benchmarks reported in the literature. On these programs, we determine whether race-freedom, atomicity or refinement is the simplest and most appropriate criterion for program correctness. Our purpose is to demonstrate empirically the fact that the appropriate fix for a concurrency error and the selection of a program analysis tool for detecting such an error must be based on the proper expression of the designer's intent using a formal correctness criterion.Publication Metadata only A novel test coverage metric for concurrently-accessed software components (A work-in-progress paper)(Springer-Verlag Berlin, 2006) N/A; Department of Computer Engineering; N/A; Department of Computer Engineering; Department of Computer Engineering; Taşıran, Serdar; Elmas, Tayfun; Bölükbaşı, Güven; Keremoğlu, M. Erkan; Faculty Member; PhD Student; Undergraduate Student; Reseacher; Department of Computer Engineering; College of Engineering; Graduate School of Sciences and Engineering; College of Engineering, College of Engineering; N/A; N/A; N/A; N/AWe propose a novel, practical coverage metric called "location pairs" (LP) for concurrently-accessed software components. The LP metric captures well common concurrency errors that lead to atomicity or refinement violations. We describe a software tool for measuring LP coverage and outline an inexpensive application of predicate abstraction and model checking for ruling out infeasible coverage targets.Publication Metadata only A prediction framework for fast sparse triangular solves(Springer International Publishing Ag, 2020) N/A; N/A; Department of Computer Engineering; Ahmad, Najeeb; Yılmaz, Buse; Erten, Didem Unat; PhD Student; N/A; Faculty Member; Department of Computer Engineering; Graduate School of Sciences and Engineering; N/A; College of Engineering; N/A; N/A; 219274Sparse triangular solve (SpTRSV) is an important linear algebra kernel, finding extensive uses in numerical and scientific computing. The parallel implementation of SpTRSV is a challenging task due to the sequential nature of the steps involved. This makes it, in many cases, one of the most time-consuming operations in an application. Many approaches for efficient SpTRSV on CPU and GPU systems have been proposed in the literature. However, no single implementation or platform (CPU or GPU) gives the fastest solution for all input sparse matrices. In this work, we propose a machine learning-based framework to predict the SpTRSV implementation giving the fastest execution time for a given sparse matrix based on its structural features. The framework is tested with six SpTRSV implementations on a state-of-the-art CPU-GPU machine (Intel Xeon Gold CPU, NVIDIA V100 GPU). Experimental results, with 998 matrices taken from the SuiteSparse Matrix Collection, show the classifier prediction accuracy of 87% for the fastest SpTRSV algorithm for a given input matrix. Predicted SpTRSV implementations achieve average speedups (harmonic mean) in the range of 1.4-2.7x against the six SpTRSV implementations used in the evaluation.Publication Metadata only Adaptive level binning: a new algorithm for solving sparse triangular systems(Information Processing Society of Japan (IPSJ), 2020) Department of Computer Engineering; Department of Computer Engineering; N/A; Department of Computer Engineering; Erten, Didem Unat; Yılmaz, Buse; Ahmad, Najeeb; Sipahioğlu, Buğra; Faculty Member; Researcher; PhD Student; Undergraduate Student; Department of Computer Engineering; College of Engineering; College of Engineering; Graduate School of Sciences and Engineering; College of Engineering; 219274; N/A; N/A; N/ASparse triangular solve (SpTRSV) is an important scientific kernel used in several applications such as preconditioners for Krylov methods. Parallelizing SpTRSV on multi-core systems is challenging since it exhibits limited parallelism due to computational dependencies and introduces high parallelization overhead due to finegrained and unbalanced nature of workloads. We propose a novel method, named Adaptive Level Binning (ALB), that addresses these challenges by eliminating redundant synchronization points and adapting the work granularity with an efficient load balancing strategy. Similar to the commonly used level-set methods for solving SpTRSV, ALB constructs level-sets of rows, where each level can be computed in parallel. Differently, ALB bins rows to levels adaptively and reduces redundant dependencies between rows. On an Intel® Xeon® Gold 6148 processor and NVIDIA® Tesla V100 GPU, ALB obtains 1.83x speedup on average and up to 5.28x speedup over Intel MKL and, over NVIDIA cuSPARSE, an average speedup of 2.80x and a maximum speedup of 39.40x for 29 matrices selected from Suite Sparse Matrix Collection.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 An efficient 2-party private function evaluation protocol based on half gates(Oxford Univ Press, 2019) Bingol, Muhammed Ali; Kiraz, Mehmet Sabir; Levi, Albert; N/A; Biçer, Osman; PhD Student; Graduate School of Sciences and Engineering; N/APrivate function evaluation (PFE) is a special case of secure multi-party computation (MPC), where the function to be computed is known by only one party. PFE is useful in several real-life applications where an algorithm or a function itself needs to remain secret for reasons such as protecting intellectual property or security classification level. In this paper, we focus on improving 2-party PFE based on symmetric cryptographic primitives. In this respect, we look back at the seminal PFE framework presented by Mohassel and Sadeghian at Eurocrypt'13. We show how to adapt and utilize the well-known half gates garbling technique (Zahur et al., Eurocrypt'15) to their constant-round 2-party PFE scheme. Compared to their scheme, our resulting optimization significantly improves the efficiency of both the underlying Oblivious Evaluation of Extended Permutation (OEP) and secure 2-party computation (2PC) protocols, and yields a more than 40% reduction in overall communication cost (the computation time is also slightly decreased and the number of rounds remains unchanged).Publication Metadata only An optimization framework for QoS-enabled adaptive video streaming over openflow networks(Institute of Electrical and Electronics Engineers (IEEE), 2013) Civanlar, Seyhan; N/A; Department of Electrical and Electronics Engineering; Eğilmez, Hilmi Enes; Tekalp, Ahmet Murat; Master Student; Faculty Member; Department of Electrical and Electronics Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 26207OpenFlow is a programmable network protocol and associated hardware designed to effectively manage and direct traffic by decoupling control and forwarding layers of routing. This paper presents an analytical framework for optimization of forwarding decisions at the control layer to enable dynamic Quality of Service (QoS) over OpenFlow networks and discusses application of this framework to QoS-enabled streaming of scalable encoded videos with two QoS levels. We pose and solve optimization of dynamic QoS routing as a constrained shortest path problem, where we treat the base layer of scalable encoded video as a level-1 QoS flow, while the enhancement layers can be treated as level-2 QoS or best-effort flows. We provide experimental results which show that the proposed dynamic QoS framework achieves significant improvement in overall quality of streaming of scalable encoded videos under various coding configurations and network congestion scenarios.