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Permanent URI for this collectionhttps://hdl.handle.net/20.500.14288/3
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Publication Metadata only Subspace-based techniques for retrieval of general 3D models(IEEE, 2009) Sankur, Bülent; Dutaǧac, Helin; Department of Computer Engineering; Yemez, Yücel; Faculty Member; Department of Computer Engineering; College of Engineering; 107907In this paper we investigate the potential of subspace techniques, such as, PCA, ICA and NMF in the case of indexing and retrieval of generic 3D models. We address the 3D shape alignment problem via continuous PCA and the exhaustive axis labeling and reflections. We find that the most propitious 3D distance transform leading to discriminative subspace features is the inverse distance transform. Our performance on the Princeton Shape Benchmark is on a par with the state-of-the-art methods. ©2009 IEEE.Publication Metadata only Affect burst detection using multi-modal cues(IEEE, 2015) Department of Computer Engineering; Department of Computer Engineering; N/A; Department of Computer Engineering; N/A; Sezgin, Tevfik Metin; Yemez, Yücel; Türker, Bekir Berker; Erzin, Engin; Marzban, Shabbir; Faculty Member; Faculty Member; PhD Student; Faculty Member; Master Student; Department of Computer Engineering; College of Engineering; College of Engineering; Graduate School of Sciences and Engineering; College of Engineering; Graduate School of Sciences and Engineering; 18632; 107907; N/A; 34503; N/ARecently, affect bursts have gained significant importance in the field of emotion recognition since they can serve as prior in recognising underlying affect bursts. In this paper we propose a data driven approach for detecting affect bursts using multimodal streams of input such as audio and facial landmark points. The proposed Gaussian Mixture Model based method learns each modality independently followed by combining the probabilistic outputs to form a decision. This gives us an edge over feature fusion based methods as it allows us to handle events when one of the modalities is too noisy or not available. We demonstrate robustness of the proposed approach on 'Interactive emotional dyadic motion capture database' (IEMOCAP) which contains realistic and natural dyadic conversations. This database is annotated by three annotators to segment and label affect bursts to be used for training and testing purposes. We also present performance comparison between SVM based methods and GMM based methods for the same configuration of experiments.Publication Metadata only Determination of the correspondence between mobility (rigidity) and conservation of the interface residues(IEEE, 2010) N/A; Department of Chemical and Biological Engineering; Department of Computer Engineering; N/A; Keskin, Özlem; Gürsoy, Attila; Makinacı, Gözde Kar; Faculty Member; Faculty Member; PhD Student; Department of Chemical and Biological Engineering; Department of Computer Engineering; College of Engineering; College of Engineering; Graduate School of Sciences and Engineering; 26605; 8745; N/AHot spots at protein interfaces may play specific functional roles and contribute to the stability of the protein complex. These residues are not homogeneously distributed along the protein interfaces; rather they are clustered within locally tightly packed regions forming a network of interactions among themselves. Here, we investigate the organization of computational hot spots at protein interfaces. A list of proteins whose free and bound forms exist is examined. Inter-residue distances of the interface residues are compared for both forms. Results reveal that there exist rigid block regions at protein interfaces. More interestingly, these regions correspond to computational hot regions. Hot spots can be determined with an average positive predictive value (PPV) of 0.73 and average sensitivity value of 0.70 for seven protein complexes.Publication Metadata only Artificial bandwidth extension of speech excitation(IEEE, 2015) Department of Computer Engineering; N/A; Erzin, Engin; Turan, Mehmet Ali Tuğtekin; Faculty Member; PhD Student; Department of Computer Engineering; College of Engineering; Graduate School of Sciences and Engineering; 34503; N/AIn this paper, a new approach that extends narrowband excitation signals to synthesize wide-band speech have been proposed. Bandwidth extension problem is analyzed using source-filter separation framework where a speech signal is decomposed into two independent components. For spectral envelope extension, our former work based on hidden Markov model have been used. For excitation signal extension, the proposed method moves the spectrum based on correlation analysis where the distance between the harmonics and the structure of the excitation signal are preserved in high-bands. In experimental studies, we also apply two other well-known extension techniques for excitation signals comparatively and evaluate the overall performance of proposed system using the PESQ metric. Our findings indicate that the proposed extension method outperforms other two techniques. © 2015 IEEE./ Öz: Bu çalışmada dar bantlı kaynak sinyallerinin bant genişliği artırılarak geniş bantlı konuşma sentezleyen yeni bir yaklaşım önerilmektedir. Bant genişletme problemi kaynak süzgeç analizinin yardımıyla iki bağımsız bileşen üzerinde ayrı ayrı ele alınmıştır. Süzgeç yapısını şekillendiren izgesel zarfı, saklı Markov modeli tabanlı geçmiş çalışmamızı kullanarak iyileştirirken, dar bantlı kaynak sinyalinin genişletilmesi için izgesel kopyalamaya dayalı yeni bir yöntem öneriyoruz. Bu yeni yöntemde dar bantlı kaynak sinyalinin yüksek frekans bileşenlerindeki harmonik yapısını, ilinti analizi ile genişletip geniş bantlı kaynak sinyali sentezlemekteyiz. Öne sürülen bu iyileştirmenin başarımını ölçebilmek için literatürde sıklıkla kullanılan iki ayrı genişletme yöntemi de karşılaştırmalı olarak degerlendirilmekte- dir. Deneysel çalışmalarda öne sürdüğümüz genişletmenin PESQ ölçütüyle nesnel başarımı gösterilmiştir.Publication Metadata only RegMT system for machine translation, system combination, and evaluation(Association for Computational Linguistics, 2011) Department of Computer Engineering; Yüret, Deniz; Biçici, Ergün; Faculty Member; PhD Student; Department of Computer Engineering; College of Engineering; Graduate School of Sciences and Engineering; 179996; N/AWe present the results we obtain using our RegMT system, which uses transductive regression techniques to learn mappings between source and target features of given parallel corpora and use these mappings to generate machine translation outputs. Our training instance selection methods perform feature decay for proper selection of training instances, which plays an important role to learn correct feature mappings. RegMT uses L2 regularized regression as well as L1 regularized regression for sparse regression estimation of target features. We present translation results using our training instance selection methods, translation results using graph decoding, system combination results with RegMT, and performance evaluation with the F1 measure over target features as a metric for evaluating translation quality.Publication Metadata only KU: word sense disambiguation by substitution(Association for Computational Linguistics, 2007) Department of Computer Engineering; Yüret, Deniz; Faculty Member; Department of Computer Engineering; College of Engineering; 179996Data sparsity is one of the main factors that make word sense disambiguation (WSD) difficult. To overcome this problem we need to find effective ways to use resources other than sense labeled data. In this paper I describe a WSD system that uses a statistical language model based on a large unannotated corpus. The model is used to evaluate the likelihood of various substitutes for a word in a given context. These likelihoods are then used to determine the best sense for the word in novel contexts. The resulting system participated in three tasks in the SemEval 2007 workshop. The WSD of prepositions task proved to be challenging for the system, possibly illustrating some of its limitations: e.g. not all words have good substitutes. The system achieved promising results for the English lexical sample and English lexical substitution tasks.Publication Metadata only SemEval-2007 task 04: classification of semantic relations between nominals(Association for Computational Linguistics, 2007) Girju, Roxana; Nakov, Preslav; Nastase, Vivi; Szpakowicz, Stan; Turney, Peter; Department of Computer Engineering; Yüret, Deniz; Faculty Member; Department of Computer Engineering; College of Engineering; 179996The NLP community has shown a renewed interest in deeper semantic analyses, among them automatic recognition of relations between pairs of words in a text. We present an evaluation task designed to provide a framework for comparing different approaches to classifying semantic relations between nominals in a sentence. This is part of SemEval, the 4th edition of the semantic evaluation event previously known as SensEval. We define the task, describe the training/test data and their creation, list the participating systems and discuss their results. There were 14 teams who submitted 15 systems.Publication Metadata only Instance selection for machine translation using feature decay algorithms(Association for Computational Linguistics, 2011) Department of Computer Engineering; Yüret, Deniz; Biçici, Ergün; Faculty Member; PhD Student; Department of Computer Engineering; College of Engineering; Graduate School of Sciences and Engineering; 179996; N/AWe present an empirical study of instance selection techniques for machine translation. In an active learning setting, instance selection minimizes the human effort by identifying the most informative sentences for translation. In a transductive learning setting, selection of training instances relevant to the test set improves the final translation quality. After reviewing the state of the art in the field, we generalize the main ideas in a class of instance selection algorithms that use feature decay. Feature decay algorithms increase diversity of the training set by devaluing features that are already included. We show that the feature decay rate has a very strong effect on the final translation quality whereas the initial feature values, inclusion of higher order features, or sentence length normalizations do not. We evaluate the best instance selection methods using a standard Moses baseline using the whole 1.6 million sentence English-German section of the Europarl corpus. We show that selecting the best 3000 training sentences for a specific test sentence is sufficient to obtain a score within 1 BLEU of the baseline, using 5% of the training data is sufficient to exceed the baseline, and a ∼ 2 BLEU improvement over the baseline is possible by optimally selected subset of the training data. In out-of-domain translation, we are able to reduce the training set size to about 7% and achieve a similar performance with the baseline.Publication Metadata only Multimodal dance choreography model(IEEE, 2011) Department of Electrical and Electronics Engineering; Department of Computer Engineering; Department of Computer Engineering; Tekalp, Ahmet Murat; Erzin, Engin; Yemez, Yücel; Ofli, Ferda; Faculty Member; Faculty Member; Faculty Member; PhD Student; Department of Electrical and Electronics Engineering; Department of Computer Engineering; College of Engineering; College of Engineering; College of Engineering; Graduate School of Sciences and Engineering; 26207; 34503; 107907; N/AWe target to learn correlation models between music and dance performances to synthesize music driven dance choreographies. The proposed framework learns statistical mappings from musical measures to dance figures using musical measure models, exchangeable figures model, choreography model and dance figure models. Alternative dance choreographies are synthesized based on these statistical mappings. Objective and subjective evaluation results demonstrate that the proposed framework successfully synthesize music-driven choreographies.Publication Metadata only Comparison of Bittorrent packet traffic characteristics over IPv6 and IPv4(IEEE, 2009) Çiflikli, Cebrail; Gezer, Ali; Özşahin, A. Tuncay; Department of Computer Engineering; Özkasap, Öznur; Faculty Member; Department of Computer Engineering; College of Engineering; 113507Nowadays, BitTorrent packets constitutes a large part of peer-to-peer application traffic on the Internet. Due to increasing size of BitTorrent traffic, it becomes inevitable to take into account the effects of it in network management. Generally, studies relevant with Bittorrent traffic measurement have performed analysis with packets transmitted via IPv4 protocol. However, provided many facilities for IPv6 internet connection, its traffic volume in operational networks is increasing day by day. In IPv6 protocol, addressing strategy and most of the fields in packet header are totally changed. How these changes affect the characteristics of the Internet traffic should be understood firmly for efficient resource sharing in networks. In this study, we investigate the Bittorrent packet traffic characteristics in terms of autocorrelation, power spectral density and self similarity of packet size and packet interarrival time. We also performed a detailed comparison between IPv4 and IPv6 BitTorrent packet traffic.