Publications with Fulltext
Permanent URI for this collectionhttps://hdl.handle.net/20.500.14288/6
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Publication Open Access User interface paradigms for visually authoring mid-air gestures: a survey and a provocation(CEUR-WS, 2014) Department of Media and Visual Arts; Department of Computer Engineering; Baytaş, Mehmet Aydın; Yemez, Yücel; Özcan, Oğuzhan; Faculty Member; Faculty Member; Department of Media and Visual Arts; Department of Computer Engineering; College of Social Sciences and Humanities; College of Engineering; N/A; N/A; 12532Gesture authoring tools enable the rapid and experiential prototyping of gesture-based interfaces. We survey visual authoring tools for mid-air gestures and identify three paradigms used for representing and manipulating gesture information: graphs, visual markup languages and timelines. We examine the strengths and limitations of these approaches and we propose a novel paradigm to authoring location-based mid-air gestures based on space discretization.Publication Open Access Engagement rewarded actor-critic with conservative Q-learning for speech-driven laughter backchannel generation(Association for Computing Machinery (ACM), 2021) Department of Computer Engineering; Bayramoğlu, Öykü Zeynep; Erzin, Engin; Sezgin, Tevfik Metin; Yemez, Yücel; Faculty Member; Faculty Member; Faculty Member; Department of Computer Engineering; Koç Üniversitesi İş Bankası Yapay Zeka Uygulama ve Araştırma Merkezi (KUIS AI)/ Koç University İş Bank Artificial Intelligence Center (KUIS AI); College of Engineering; Graduate School of Sciences and Engineering; N/A; 34503; 18632; 107907We propose a speech-driven laughter backchannel generation model to reward engagement during human-agent interaction. We formulate the problem as a Markov decision process where speech signal represents the state and the objective is to maximize human engagement. Since online training is often impractical in the case of human-agent interaction, we utilize the existing human-to-human dyadic interaction datasets to train our agent for the backchannel generation task. We address the problem using an actor-critic method based on conservative Q-learning (CQL), that mitigates the distributional shift problem by suppressing Q-value over-estimation during training. The proposed CQL based approach is evaluated objectively on the IEMOCAP dataset for laughter generation task. When compared to the existing off-policy Q-learning methods, we observe an improved compliance with the dataset in terms of laugh generation rate. Furthermore, we show the effectiveness of the learned policy by estimating the expected engagement using off-policy policy evaluation techniques.Publication Open Access The structural basis of Akt PH domain interaction with calmodulin(Elsevier, 2021) Jang, Hyunbum; Nussinov, Ruth; N/A; Department of Chemical and Biological Engineering; Department of Computer Engineering; Weako, Jackson; Keskin, Özlem; Gürsoy, Attila; Faculty Member; Department of Chemical and Biological Engineering; Department of Computer Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 26605; 8745Akt plays a key role in the Ras/PI3K/Akt/mTOR signaling pathway. In breast cancer, Akt translocation to the plasma membrane is enabled by the interaction of its pleckstrin homology domain (PHD) with calmodulin (CaM). At the membrane, the conformational change promoted by PIP3 releases CaM and facilitates Thr308 and Ser473 phosphorylation and activation. Here, using modeling and molecular dynamics simulations, we aim to figure out how CaM interacts with Akt's PHD at the atomic level. Our simulations show that CaM-PHD interaction is thermodynamically stable and involves a beta-strand rather than an alpha-helix, in agreement with NMR data, and that electrostatic and hydrophobic interactions are critical. The PHD interacts with CaM lobes; however, multiple modes are possible. IP4, the polar head of PIP3, weakens the CaM-PHD interaction, implicating the release mechanism at the plasma membrane. Recently, we unraveled the mechanism of PI3K alpha activation at the atomistic level and the structural basis for Ras role in the activation. Here, our atomistic structural data clarify the mechanism of how CaM interacts, delivers, and releases Akt-the next node in the Ras/PI3K pathway-at the plasma membrane.Publication Open Access The noisy channel mode for unsupervised word sense disambiguation(Massachusetts Institute of Technology (MIT) Press, 2010) Department of Computer Engineering; Yüret, Deniz; Yatbaz, Mehmet Ali; Faculty Member; PhD Student; Department of Computer Engineering; College of Engineering; 179996; 192506We introduce a generative probabilistic model, the noisy channel model, for unsupervised word sense disambiguation. In our model, each context C is modeled as a distinct channel through which the speaker intends to transmit a particular meaning S using a possibly ambiguous word W. To reconstruct the intended meaning the hearer uses the distribution of possible meanings in the given context P(S|C) and possible words that can express each meaning P(W|S). We assume P(W|S) is independent of the context and estimate it using WordNet sense frequencies. The main problem of unsupervised WSD is estimating context-dependent P(S|C) without access to any sense-tagged text. We show one way to solve this problem using a statistical language model based on large amounts of untagged text. Our model uses coarse-grained semantic classes for S internally and we explore the effect of using different levels of granularity on WSD performance. The system outputs fine-grained senses for evaluation, and its performance on noun disambiguation is better than most previously reported unsupervised systems and close to the best supervised systems.Publication Open Access Kart-ON: an extensible paper programming strategy for affordable early programming education(Association for Computing Machinery (ACM), 2022) Department of Computer Engineering; Sezgin, Tevfik Metin; Sabuncuoğlu, Alpay; Faculty Member; Department of Computer Engineering; Koç Üniversitesi İş Bankası Yapay Zeka Uygulama ve Araştırma Merkezi (KUIS AI)/ Koç University İş Bank Artificial Intelligence Center (KUIS AI); College of Engineering; Graduate School of Sciences and Engineering; 18632; N/AProgramming has become a core subject in primary and middle school curricula. Yet, conventional solutions for in-class programming activities require each student to have expensive equipment, which creates an opportunity gap for low-income students. Paper programming can provide an affordable, engaging, and collaborative in-class programming experience by allowing groups of students to use inexpensive materials and share smartphones. However, current paper-programming examples are limited in terms of language expressivity and generalizability. Addressing these limitations, we developed a paper-programming flow and its variants in different abstraction levels and input/output styles. The programming environments consist of pre-defined tangible programming cards and a mobile application that runs computer vision models to recognize them. This paper describes our educational and technical development process, presents a qualitative analysis of the early user study results and shares our design considerations to help develop wide-reaching paper programming environments.Publication Open Access Tree-stack LSTM in transition based dependency parsing(Association for Computational Linguistics (ACL), 2018) Department of Computer Engineering; N/A; Yüret, Deniz; Faculty Member; Department of Computer Engineering; College of Engineering; Graduate School of Sciences and Engineering; 179996; N/AWe introduce tree-stack LSTM to model state of a transition based parser with recurrent neural networks. Tree-stack LSTM does not use any parse tree based or hand-crafted features, yet performs better than models with these features. We also develop new set of embeddings from raw features to enhance the performance. There are 4 main components of this model: stack's σ-LSTM, buffer's βLSTM, actions' LSTM and tree-RNN. All LSTMs use continuous dense feature vectors (embeddings) as an input. Tree-RNN updates these embeddings based on transitions. We show that our model improves performance with low resource languages compared with its predecessors. We participate in CoNLL 2018 UD Shared Task as the”KParse” team and ranked 16th in LAS, 15th in BLAS and BLEX metrics, of 27 participants parsing 82 test sets from 57 languages.Publication Open Access Multiple shape correspondence by dynamic programming(Wiley, 2014) Sahillioğlu, Y.; Department of Computer Engineering; Yemez, Yücel; Faculty Member; Department of Computer Engineering; College of EngineeringWe present a multiple shape correspondence method based on dynamic programming, that computes consistent bijective maps between all shape pairs in a given collection of initially unmatched shapes. As a fundamental distinction from previous work, our method aims to explicitly minimize the overall distortion, i.e., the average isometric distortion of the resulting maps over all shape pairs. We cast the problem as optimal path finding on a graph structure where vertices are maps between shape extremities. We exploit as much context information as possible using a dynamic programming based algorithm to approximate the optimal solution. Our method generates coarse multiple correspondences between shape extremities, as well as denser correspondences as by-product. We assess the performance on various mesh sequences of (nearly) isometric shapes. Our experiments show that, for isometric shape collections with non-uniform triangulation and noise, our method can compute relatively dense correspondences reasonably fast and outperform state of the art in terms of accuracy.Publication Open Access Observation of the correlations between pair wise interaction and functional organization of the proteins, in the protein ınteraction network of saccaromyces cerevisiae(World Academy of Science, Engineering and Technology (WASET), 2008) Haliloğlu, T.; Department of Computer Engineering; Department of Chemical and Biological Engineering; Tunçbağ, Nurcan; Keskin, Özlem; Faculty Member; Department of Computer Engineering; Department of Chemical and Biological Engineering; College of Engineering; N/A; 26605Understanding the cell's large-scale organization is an interesting task in computational biology. Thus, protein-protein interactions can reveal important organization and function of the cell. Here, we investigated the correspondence between protein interactions and function for the yeast. We obtained the correlations among the set of proteins. Then these correlations are clustered using both the hierarchical and biclustering methods. The detailed analyses of proteins in each cluster were carried out by making use of their functional annotations. As a result, we found that some functional classes appear together in almost all biclusters. On the other hand, in hierarchical clustering, the dominancy of one functional class is observed. In brief, from interaction data to function, some correlated results are noticed about the relationship between interaction and function which might give clues about the organization of the proteins.Publication Open Access Leveraging frequency based salient spatial sound localization to improve 360 degrees video saliency prediction(Institute of Electrical and Electronics Engineers (IEEE), 2021) Çökelek, Mert; İmamoğlu, Nevrez; Özçınar, Çağrı; Department of Computer Engineering; Erdem, Aykut; Faculty Member; Department of Computer Engineering; College of Engineering; 20331Virtual and augmented reality (VR/AR) systems dramatically gained in popularity with various application areas such as gaming, social media, and communication. It is therefore a crucial task to have the knowhow to efficiently utilize, store or deliver 360° videos for end-users. Towards this aim, researchers have been developing deep neural network models for 360° multimedia processing and computer vision fields. In this line of work, an important research direction is to build models that can learn and predict the observers' attention on 360° videos to obtain so-called saliency maps computationally. Although there are a few saliency models proposed for this purpose, these models generally consider only visual cues in video frames by neglecting audio cues from sound sources. In this study, an unsupervised frequency-based saliency model is presented for predicting the strength and location of saliency in spatial audio. The prediction of salient audio cues is then used as audio bias on the video saliency predictions of state-of-the-art models. Our experiments yield promising results and show that integrating the proposed spatial audio bias into the existing video saliency models consistently improves their performance.Publication Open Access HotRegion: a database of predicted hot spot clusters(Oxford University Press (OUP), 2012) N/A; Department of Computer Engineering; Department of Chemical and Biological Engineering; Çukuroğlu, Engin; Gürsoy, Attila; Keskin, Özlem; PhD Student; Faculty Member; Department of Computer Engineering; Department of Chemical and Biological Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 8745; 26605Hot spots are energetically important residues at protein interfaces and they are not randomly distributed across the interface but rather clustered. These clustered hot spots form hot regions. Hot regions are important for the stability of protein complexes, as well as providing specificity to binding sites. We propose a database called HotRegion, which provides the hot region information of the interfaces by using predicted hot spot residues, and structural properties of these interface residues such as pair potentials of interface residues, accessible surface area (ASA) and relative ASA values of interface residues of both monomer and complex forms of proteins. Also, the 3D visualization of the interface and interactions among hot spot residues are provided.