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Publication Metadata only A survey of energy efficiency in SDN: Software-based methods and optimization models(Elsevier, 2019) N/A; N/A; Department of Computer Engineering; Assefa, Beakal Gizachew; Özkasap, Öznur; PhD Student; Faculty Member; Department of Computer Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; N/A; 113507Software Defined Networking (SDN) paradigm has the benefits of programmable network elements by separating the control and the forwarding planes, efficiency through optimized routing and flexibility in network management. As the energy costs contribute largely to the overall costs in networks, energy efficiency has become a significant design requirement for modem networking mechanisms. However, designing energy efficient solutions is non-trivial since they need to tackle the trade-off between energy efficiency and network performance. In this article, we address the energy efficiency capabilities that can be utilized in the emerging SDN. We provide a comprehensive and novel classification of software-based energy efficient solutions into subcategories of traffic aware, end system aware and rule placement. We propose general optimization models for each subcategory, and present the objective function, the parameters and constraints to be considered in each model. Detailed information on the characteristics of state-of-the-art methods, their advantages, drawbacks are provided. Hardware-based solutions used to enhance the efficiency of switches are also described. Furthermore, we discuss the open issues and future research directions in the area of energy efficiency in SDN.Publication Open Access A task set proposal for automatic protest information collection across multiple countries(Springer, 2019) Department of Sociology; Department of Computer Engineering; Hürriyetoğlu, Ali; Yörük, Erdem; Yoltar, Çağrı; Yüret, Deniz; Gürel, Burak; Duruşan, Fırat; Mutlu, Osman; Teaching Faculty; Faculty Member; Researcher; Faculty Member; Faculty Member; Researcher; Department of Sociology; Department of Computer Engineering; Graduate School of Social Sciences and Humanities; Graduate School of Sciences and Engineering; N/A; 28982; N/A; 179996; 219277; N/A; N/AWe propose a coherent set of tasks for protest information collection in the context of generalizable natural language processing. The tasks are news article classification, event sentence detection, and event extraction. Having tools for collecting event information from data produced in multiple countries enables comparative sociology and politics studies. We have annotated news articles in English from a source and a target country in order to be able to measure the performance of the tools developed using data from one country on data from a different country. Our preliminary experiments have shown that the performance of the tools developed using English texts from India drops to a level that are not usable when they are applied on English texts from China. We think our setting addresses the challenge of building generalizable NLP tools that perform well independent of the source of the text and will accelerate progress in line of developing generalizable NLP systems.Publication Open Access Audiovisual synchronization and fusion using canonical correlation analysis(Institute of Electrical and Electronics Engineers (IEEE), 2007) Department of Computer Engineering; Department of Electrical and Electronics Engineering; Sargın, Mehmet Emre; Yemez, Yücel; Erzin, Engin; Tekalp, Ahmet Murat; Faculty Member; Faculty Member; Faculty Member; Department of Computer Engineering; Department of Electrical and Electronics Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; N/A; 34503; 26207It is well-known that early integration (also called data fusion) is effective when the modalities are correlated, and late integration (also called decision or opinion fusion) is optimal when modalities are uncorrelated. In this paper, we propose a new multimodal fusion strategy for open-set speaker identification using a combination of early and late integration following canonical correlation analysis (CCA) of speech and lip texture features. We also propose a method for high precision synchronization of the speech and lip features using CCA prior to the proposed fusion. Experimental results show that i) the proposed fusion strategy yields the best equal error rates (EER), which are used to quantify the performance of the fusion strategy for open-set speaker identification, and ii) precise synchronization prior to fusion improves the EER; hence, the best EER is obtained when the proposed synchronization scheme is employed together with the proposed fusion strategy. We note that the proposed fusion strategy outperforms others because the features used in the late integration are truly uncorrelated, since they are output of the CCA analysis.Publication Open Access Boxlib with tiling: an adaptive mesh refinement software framework(Society for Industrial and Applied Mathematics (SIAM), 2016) Zhang, W.; Almgren, A.; Day, M.; Nguyen, T.; Shalf, J.; Department of Computer Engineering; Erten, Didem Unat; Faculty Member; Department of Computer Engineering; College of Engineering; 219274In this paper we introduce a block-structured adaptive mesh refinement software framework that incorporates tiling, a well-known loop transformation. Because the multiscale, multiphysics codes built in boxlib are designed to solve complex systems at high resolution, performance on current and next generation architectures is essential. With the expectation of many more cores per node on next generation architectures, the ability to effectively utilize threads within a node is essential, and the current model for parallelization will not be sufficient. We describe a new version of boxlib in which the tiling constructs are embedded so that boxlib-based applications can easily realize expected performance gains without extra effort on the part of the application developer. We also discuss a path forward to enable future versions of boxlib to take advantage of NUMA-aware optimizations using the tida portable library.Publication Open Access 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 Computer Engineering; Hürriyetoğlu, Ali; Yörük, Erdem; Mutlu, Osman; Yüret, Deniz; Teaching Faculty; Faculty Member; Researcher; Faculty Member; Department of Sociology; Department of Computer Engineering; 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 Coarse-to-fine surface reconstruction from silhouettes and range data using mesh deformation(Academic Press Inc Elsevier Science, 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 a coarse-to-fine surface reconstruction method based on mesh deformation to build watertight surface models of complex objects from their silhouettes and range data. The deformable mesh, which initially represents the object visual hull, is iteratively displaced towards the triangulated range surface using the line-of-sight information. Each iteration of the deformation algorithm involves smoothing and restructuring operations to regularize the surface evolution process. We define a non-shrinking and easy-to-compute smoothing operator that fairs the surface separately along its tangential and normal directions. The mesh restructuring operator, which is based on edge split, collapse and flip operations, enables the deformable mesh to adapt its shape to the object geometry without suffering from any geometrical distortions. By imposing appropriate minimum and maximum edge length constraints, the deformable mesh, hence the object surface, can be represented at increasing levels of detail. This coarse-to-fine strategy, that allows high resolution reconstructions even with deficient and irregularly sampled range data, not only provides robustness, but also significantly improves the computational efficiency of the deformation process. We demonstrate the performance of the proposed method on several real objects.Publication Open Access Cross-context news corpus for protest event-related knowledge base construction(Massachusetts Institute of Technology (MIT) Press, 2021) Department of Sociology; N/A; Department of Computer Engineering; Yörük, Erdem; Hürriyetoğlu, Ali; Gürel, Burak; Duruşan, Fırat; Yoltar, Çağrı; Mutlu, Osman; Yüret, Deniz; Faculty Member; Teaching Faculty; Faculty Member; Researcher; Researcher; Faculty Member; Department of Sociology; Department of Computer Engineering; College of Social Sciences and Humanities; Graduate School of Sciences and Engineering; College of Engineering; 28982; N/A; 219277; N/A; N/A; N/A; 179996We describe a gold standard corpus of protest events that comprise various local and international English language sources from various countries. The corpus contains document-, sentence-, and token-level annotations. This corpus facilitates creating machine learning models that automatically classify news articles and extract protest event-related information, constructing knowledge bases that enable comparative social and political science studies. For each news source, the annotation starts with random samples of news articles and continues with samples drawn using active learning. Each batch of samples is annotated by two social and political scientists, adjudicated by an annotation supervisor, and improved by identifying annotation errors semi-automatically. We found that the corpus possesses the variety and quality that are necessary to develop and benchmark text classification and event extraction systems in a cross-context setting, contributing to the generalizability and robustness of automated text processing systems. This corpus and the reported results will establish a common foundation in automated protest event collection studies, which is currently lacking in the literature.Publication Open Access Cross-lingual visual pre-training for multimodal machine translation(Association for Computational Linguistics (ACL), 2021) Çağlayan, O.; Kuyu, M.; Amaç, M. S.; Madhyastha, P.; Erdem, E.; Specia, L.; Department of Computer Engineering; Erdem, Aykut; Faculty Member; Department of Computer Engineering; College of Engineering; 20331Pre-trained language models have been shown to improve performance in many natural language tasks substantially. Although the early focus of such models was single language pre-training, recent advances have resulted in cross-lingual and visual pre-training methods. In this paper, we combine these two approaches to learn visually-grounded cross-lingual representations. Specifically, we extend the translation language modelling (Lample and Conneau, 2019) with masked region classification and perform pre-training with three-way parallel vision & language corpora. We show that when fine-tuned for multimodal machine translation, these models obtain state-of-the-art performance. We also provide qualitative insights into the usefulness of the learned grounded representations.Publication Metadata only Cutting down the energy cost of geographically distributed cloud data centers(Springer-Verlag Berlin, 2013) Cambazoğlu, Berkant Barla; N/A; Department of Computer Engineering; Güler, Hüseyin; Özkasap, Öznur; PhD Student; Faculty Member; Department of Computer Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 113507The energy costs constitute a significant portion of the total cost of cloud providers. The major cloud data centers are often geographically distributed, and this brings an opportunity to minimize their energy cost. In this work, we model a geographically distributed data center network that is specialized to run batch jobs. Taking into account the spatio-temporal variation in the electricity prices and the outside weather temperature, we model the problem of minimizing the energy cost as a linear programming problem. We propose various heuristic solutions for the problem. Our simulations using real-life workload traces and electricity prices demonstrate that the proposed heuristics can considerably decrease the total energy cost of geographically distributed cloud data centers, compared to a baseline technique.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.