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Publication Metadata only BindMe: a thread binding library with advanced mapping algorithms(Wiley, 2018) N/A; Department of Computer Engineering; Department of Computer Engineering; Soomro, Pirah Noor; Sasongko, Muhammad Aditya; Erten, Didem Unat; PhD Student; Researcher; Faculty Member; Department of Computer Engineering; Graduate School of Sciences and Engineering; College of Engineering; College of Engineering; N/A; N/A; 219274Binding parallel tasks to cores according to a placement policy is one of the key aspects to achieve good performance in multicore machines because it can reduce on-chip communication among parallel threads. Binding also prevents operating system from migrating threads, which improves data locality. However, there is no single mapping policy that works best among all different kinds of applications and platforms because each machine has a different topology and each application exhibits different communication pattern. Determining the best policy for a given application and machine requires extra programming effort. To relieve the programmer from that burden, we introduce BindMe, A thread binding library that assists programmer to bind threads to underlying hardware. BindMe incorporates state-of-the-art mapping algorithms, which use communication pattern in an application to formulate an efficient task placement policy. We also introduce ChoiceMap, A communication aware mapping algorithm that respects mutual priorities of parallel tasks and performs a fair mapping by reducing communication volume among cores. We have tested BindMe and ChoiceMap with various applications from NaS parallel benchmark and Rodinia bechmark. Our results show that choosing a mapping policy that best suits the application behavior can increase its performance and no single policy gives the best performance across different applications.Publication Metadata only Coarse-to-fine combinatorial matching for dense isometric shape correspondence(Wiley, 2011) 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 a dense correspondence method for isometric shapes, which is accurate yet computationally efficient. We minimize the isometric distortion directly in the 3D Euclidean space, i.e., in the domain where isometry is originally defined, by using a coarse-to-fine sampling and combinatorial matching algorithm. Our method does not require any initialization and aims to find an accurate solution in the minimum-distortion sense for perfectly isometric shapes. We demonstrate the performance of our method on various isometric (or nearly isometric) pairs of shapes.Publication Metadata only Coarse-to-fine isometric shape correspondence by tracking symmetric flips(Wiley, 2013) 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 address the symmetric flip problem that is inherent to multi-resolution isometric shape matching algorithms. To this effect, we extend our previous work which handles the dense isometric correspondence problem in the original 3D Euclidean space via coarse-to-fine combinatorial matching. The key idea is based on keeping track of all optimal solutions, which may be more than one due to symmetry especially at coarse levels, throughout denser levels of the shape matching process. We compare the resulting dense correspondence algorithm with state-of-the-art techniques over several 3D shape benchmark datasets. The experiments show that our method, which is fast and scalable, is performance-wise better than or on a par with the best performant algorithms existing in the literature for isometric (or nearly isometric) shape correspondence. Our key idea of tracking symmetric flips can be considered as a meta-approach that can be applied to other multi-resolution shape matching algorithms, as we also demonstrate by experiments.Publication Metadata only Controlling P2P-CDN live streaming services at SDN-enabled multi-access edge datacenters(Ieee-Inst Electrical Electronics Engineers Inc, 2021) N/A; N/A; Department of Electrical and Electronics Engineering; Nacaklı, Selin; Tekalp, Ahmet Murat; PhD Student; Faculty Member; Department of Electrical and Electronics Engineering; Graduate School of Sciences and Engineering; College of Engineering; 341857; 26207Recognizing the shortcomings of current hybrid peer-to-peer (P2P) content-distribution network (CDN) video solutions and the potential of emerging multi-access edge datacenters, we propose a novel P2P-CDN service model that is hosted at software defined networks (SDN)-enabled multi-access edge datacenters operated by network service providers (NSP). An important feature of the proposed service architecture is that both CDN access by peers and P2P video streaming between peers within edge access networks are fully controlled by cooperation of the video content provider (VCP) and NSP to optimize video service key performance indicators (KPI). The proposed fully controlled P2P-CDN architecture with P2P group formation and chunk scheduling managed at edge datacenters reduces the load on CDN servers while overcoming quality of experience (QoE) fluctuations per flow and unfairness between multiple heterogeneous video-resolution clients over reserved access network slices. Other advantages of this service include: i) better video quality and lower delay for clients; ii) better use of edge network resources; iii) avoiding illegal, unauthorized P2P content sharing. To the best of our knowledge, there are no solutions in the literature that address P2P-CDN services managed at NSP-edge datacenters combining P2P-assisted CDN, SDN-assisted edge computing, and premium service over reserved slices. Experimental results show that the proposed P2P-CDN service deployed at SDN-enabled edge datacenters provides excellent service KPI compared to other state-of-the-art solutions.Publication Open Access Deep generation of 3D articulated models and animations from 2D stick figures(Elsevier, 2022) Akman, Alican; Sahillioğlu, Yusuf; Department of Computer Engineering; Sezgin, Tevfik Metin; Faculty Member; Department of Computer Engineering; College of Engineering; 18632Generating 3D models from 2D images or sketches is a widely studied important problem in computer graphics. We describe the first method to generate a 3D human model from a single sketched stick figure. In contrast to the existing human modeling techniques, our method does not require a statistical body shape model. We exploit Variational Autoencoders to develop a novel framework capable of transitioning from a simple 2D stick figure sketch, to a corresponding 3D human model. Our network learns the mapping between the input sketch and the output 3D model. Furthermore, our model learns the embedding space around these models. We demonstrate that our network can generate not only 3D models, but also 3D animations through interpolation and extrapolation in the learned embedding space. In addition to 3D human models, we produce 3D horse models in order to show the generalization ability of our framework. Extensive experiments show that our model learns to generate compatible 3D models and animations with 2D sketches.Publication Metadata only Dynamic resource allocation by batch optimization for value-added video services over SDN(IEEE-Inst Electrical Electronics Engineers Inc, 2018) N/A; Department of Electrical and Electronics Engineering; Bağcı, Kadir Tolga; Tekalp, Ahmet Murat; PhD Student; Faculty Member; Department of Electrical and Electronics Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 26207We propose a video service architecture and a novel resource allocation optimization framework to enable network service providers (NSP) to offer value-added video services (VAVS) over software-defined networking including different service levels, service-level awareness of users, and associated business models. To this effect, we introduce a new batch-optimization framework, where resource (path, bitrate, and admission control) allocations for a small group of flows (consisting of new service requests and some existing ones) are performed simultaneously as the number of new service requests and network conditions vary. The optimization problem becomes NP-complete when path computations are jointly (re-)optimized as a group in order to accommodate all service requests to the extent possible, to best utilize entire network resources in a fair manner, and maximize network service provider's revenue. In order to compute dynamic resource allocations online, we propose a heuristic group-constrained-shortest path procedure that aims for a fair allocation of resources among a group of requests with the same service level, while maximizing the total NSP revenue. Experimental results demonstrate the feasibility of the proposed method for possible deployment by NSP to offer future VAVS, and that the proposed solution is close to the optimal solution, which is approximately computed using a divide-and conquer strategy, for varying network size and traffic load conditions. In particular, we show that processing service requests in batches significantly improves total revenue and fairness in congested mode of operation.Publication Open Access Federated dropout learning for hybrid beamforming with spatial path index modulation in multi-user MMWave-MIMO systems(Institute of Electrical and Electronics Engineers (IEEE), 2021) Mishra, Kumar Vijay; Department of Electrical and Electronics Engineering; Ergen, Sinem Çöleri; Elbir, Ahmet Musab; Faculty Member; Department of Electrical and Electronics Engineering; College of Engineering; 7211; N/AMillimeter wave multiple-input multiple-output (mmWave-MIMO) systems with small number of radio-frequency (RF) chains have limited multiplexing gain. Spatial path index modulation (SPIM) is helpful in improving this gain by utilizing additional signal bits modulated by the indices of spatial paths. In this paper, we introduce model-based and model-free frameworks for beamformer design in multi-user SPIM-MIMO systems. We first design the beamformers via model-based manifold optimization algorithm. Then, we leverage federated learning (FL) with dropout learning (DL) to train a learning model on the local dataset of users, who estimate the beamformers by feeding the model with their channel data. The DL randomly selects different set of model parameters during training, thereby further reducing the transmission overhead compared to conventional FL. Numerical experiments show that the proposed framework exhibits higher spectral efficiency than the state-of-the-art SPIM-MIMO methods and mmWave-MIMO, which relies on the strongest propagation path. Furthermore, the proposed FL approach provides at least 10 times lower transmission overhead than the centralized learning techniques.Publication Metadata only FlexDPDP: flexlist-based optimized dynamic provable data possession(assoc Computing Machinery, 2016) N/A; N/A; N/A; Department of Computer Engineering; Department of Computer Engineering; Department of Computer Engineering; Esiner, Ertem; Kachkeev, Adilet; Küpçü, Alptekin; Özkasap, Öznur; Master Student; Master Student; N/A; Faculty Member; Faculty Member; Department of Computer Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; College of Engineering; College of Engineering; N/A; N/A; N/A; 168060; 113507With increasing popularity of cloud storage, efficiently proving the integrity of data stored on an untrusted server has become significant. authenticated skip lists and rank-based authenticated skip lists (RBaSL) have been used to provide support for provable data update operations in cloud storage. However, in a dynamic file scenario, An RBaSL based on block indices falls short when updates are not proportional to a fixed block size; such an update to the file, even if small, may result in O(n) updates on the data structure for a file with n blocks. To overcome this problem, we introduce FlexList, A flexible length-based authenticated skip list. FlexList translates variable-size updates to O(inverted right perpendicularu/Binverted left perpendicular) insertions, removals, or modifications, where u is the size of the update and B is the (average) block size. We further present various optimizations on the four types of skip lists (regular, Authenticated, rank-based authenticated, and FlexList). We build such a structure in O(n) time and parallelize this operation for the first time. We compute one single proof to answer multiple (non) membership queries and obtain efficiency gains of 35%, 35%, and 40% in terms of proof time, energy, and size, respectively. We propose a method of handling multiple updates at once, Achieving efficiency gains of up to 60% at the server side and 90% at the client side. We also deployed our implementation of FlexDPDP (dynamic provable data possession (DPDP) with FlexList instead of RBaSL) on PlanetLab, demonstrating that FlexDPDP performs comparable to the most efficient static storage scheme (provable data possession (PDP)) while providing dynamic data support.Publication Metadata only Hybrid federated and centralized learning(European Assoc Signal Speech & Image Processing-Eurasip, 2021) Mishra, Kumar Vijay; N/A; Department of Electrical and Electronics Engineering; Elbir, Ahmet Musab; Ergen, Sinem Çöleri; N/A; Faculty Member; Department of Electrical and Electronics Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 7211Many of the machine learning tasks are focused on centralized learning (CL), which requires the transmission of local datasets from the clients to a parameter server (PS) leading to a huge communication overhead. Federated learning (FL) overcomes this issue by allowing the clients to send only the model updates to the PS instead of the whole dataset. In this way, FL brings the learning to edge level, wherein powerful computational resources are required on the client side. This requirement may not always be satisfied because of diverse computational capabilities of edge devices. We address this through a novel hybrid federated and centralized learning (HFCL) framework to effectively train a learning model by exploiting the computational capability of the clients. In HFCL, only the clients who have sufficient resources employ FL; the remaining clients resort to CL by transmitting their local dataset to PS. This allows all the clients to collaborate on the learning process regardless of their computational resources. We also propose a sequential data transmission approach with HFCL (HFCL-SDT) to reduce the training duration. The proposed HFCL frameworks outperform previously proposed non-hybrid FL (CL) based schemes in terms of learning accuracy (communication overhead) since all the clients collaborate on the learning process with their datasets regardless of their computational resources.Publication Metadata only Multi-party WebRTC videoconferencing using scalable VP9 video: from best-effort over-the-top to managed value-added services(Institute of Electrical and Electronics Engineers (IEEE), 2018) N/A; N/A; Department of Electrical and Electronics Engineering; Department of Electrical and Electronics Engineering; Kırmızıoğlu, Rıza Arda; Kaya, Barış Can; Tekalp, Ahmet Murat; Master Student; Undergraduate Student; Faculty Member; Department of Electrical and Electronics Engineering; Graduate School of Sciences and Engineering; College of Engineering; College of Engineering; N/A; N/A; 26207We propose architectures and implementations for WebRTC videoconferencing services using scalable VP9 video coding with motion-adaptive rate control as either a best-effort over-the-top service or as a managed value-added service with rate reservation over software-defined networks. In the best-effort service, clients perform motion-adaptive layer selection according to the available bandwidth in order to achieve the best overall visual video quality. In the value-added managed service, the service manager reserves bandwidth between mesh-connected clients according to rates agreed by them, and clients perform motion-adaptive layer selection to adapt their send rates to the bandwidths reserved between the end points. The proposed framework has been demonstrated to yield excellent results for both point-to-point two-party and mesh-connected multi-party videoconferencing.