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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; BaytaÅ, Mehmet Aydın; Yemez, YĆ¼cel; Ćzcan, OÄuzhan; 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; BayramoÄlu, ĆykĆ¼ Zeynep; Erzin, Engin; Sezgin, Tevfik Metin; Yemez, YĆ¼cel; Faculty Member; Faculty Member; Faculty Member; Department of Computer Engineering; KoƧ Ćniversitesi Ä°Å Bankası Yapay Zeka Uygulama ve AraÅtırma Merkezi (KUIS AI)/ KoƧ University Ä°Å 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, Ćzlem; GĆ¼rsoy, 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; YĆ¼ret, 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 Craft: a benchmark for causal reasoning about forces and in teractions(Association for Computational Linguistics (ACL), 2022) AteÅ, Tayfun; AteÅoÄlu, M. Åamil; YiÄit, ĆaÄatay; Department of Computer Engineering; Department of Psychology; Erdem, Aykut; Gƶksun, Tilbe; YĆ¼ret, Deniz; Kesen, Ä°lker; KobaÅ, Mert; Faculty Member; Faculty Member; Faculty Member; Master Student; Department of Computer Engineering; Department of Psychology; KoƧ Ćniversitesi Ä°Å Bankası Yapay Zeka Uygulama ve AraÅtırma Merkezi (KUIS AI)/ KoƧ University Ä°Å Bank Artificial Intelligence Center (KUIS AI); Graduate School of Sciences and Engineering; College of Engineering; College of Social Sciences and Humanities; 20331; 47278; 179996; N/A; N/A; N/AHumans are able to perceive, understand and reason about causal events. Developing models with similar physical and causal understanding capabilities is a long-standing goal of artificial intelligence. As a step towards this direction, we introduce CRAFT1, a new video question answering dataset that requires causal reasoning about physical forces and object interactions. It contains 58K video and question pairs that are generated from 10K videos from 20 different virtual environments, containing various objects in motion that interact with each other and the scene. Two question categories in CRAFT include previously studied descriptive and counterfactual questions. Additionally, inspired by the Force Dynamics Theory in cognitive linguistics, we introduce a new causal question category that involves understanding the causal interactions between objects through notions like cause, enable, and prevent. Our results show that even though the questions in CRAFT are easy for humans, the tested baseline models, including existing state-of-the-art methods, do not yet deal with the challenges posed in our benchmark.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; SabuncuoÄlu, Alpay; Faculty Member; Department of Computer Engineering; KoƧ Ćniversitesi Ä°Å Bankası Yapay Zeka Uygulama ve AraÅtırma Merkezi (KUIS AI)/ KoƧ University Ä°Å 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 GestAnalytics: experiment and analysis tool for gesture-elicitation studies(Association for Computing Machinery (ACM), 2017) Department of Computer Engineering; Buruk, OÄuz Turan; Ćzcan, OÄuzhan; Faculty Member; Department of Computer Engineering; KU ArƧelik Research Center for Creative Industries (KUAR) / KU ArƧelik Yaratıcı EndĆ¼striler Uygulama ve AraÅtırma Merkezi (KUAR); College of Engineering; N/A; 12532Gesture-elicitation studies are common and important studies for understanding user preferences. In these studies, researchers aim at extracting gestures which are desirable by users for different kinds of interfaces. During this process, researchers have to manually analyze many videos which is a tiring and a time consuming process. Although current tools for video analysis provide annotation opportunity and features like automatic gesture analysis, researchers still need to (1) divide videos into meaningful pieces, (2) manually examine each piece, (3) match collected user data with these, (4) code each video and (5) verify their coding. These processes are burdensome and current tools do not aim to make this process easier and faster. To fill this gap, we developed āGestAnalyticsā with features of simultaneous video monitoring, video tagging and filtering. Our internal pilot tests show that GestAnalytics can be a beneficial tool for researchers who practice video analysis for gestural interfaces.Publication Open Access Interlaced: fully decentralized churn stabilization for Skip Graph-based DHTs(Elsevier, 2021) Department of Computer Engineering; Hassanzadeh-Nazarabadi, Yahya; KĆ¼pĆ§Ć¼, Alptekin; Ćzkasap, Ćznur; PhD Student; Faculty Member; Department of Computer Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 168060; 113507As a distributed hash table (DHT) routing overlay, Skip Graph is used in a variety of peer-to-peer (P2P) systems including cloud storage. The overlay connectivity of P2P systems is negatively affected by the arrivals and departures of nodes to and from the system that is known as churn. Preserving connectivity of the overlay network (i.e., the reachability of every pair of nodes) under churn without compromising the overlay latency is a performance challenge in every P2P system including the Skip Graph-based ones. The existing decentralized churn stabilization solutions that are applicable to Skip Graphs mainly optimize the connectivity of the system under churn and do not consider routing latency of overlay as an optimization goal. Additionally, those existing solutions change the message complexity of Skip Graphs, distort its topology, or apply constant message overhead to the system. In this paper, we propose Interlaced, a fully decentralized churn stabilization mechanism for Skip Graphs that provides drastically stronger overlay connectivity and faster search queries without changing the asymptotic complexity of the Skip Graph in terms of storage, computation, and communication. We also propose the Sliding Window De Bruijn Graph (SWDBG ) as a tool to predict the availability of nodes with high accuracy. Our simulation results show that in comparison to the best existing DHT-based solutions, Interlaced improves the overlay connectivity of the Skip Graph under churn with the gain of about 1.73 times. Likewise, compared to the existing availability prediction approaches for P2P systems, SWDBG is about 1.26 times more accurate. A Skip Graph that benefits from Interlaced and SWDBG is about 2.47 times faster on average in routing the queries under churn compared to the best existing solutions. We also present an adaptive extension of Interlaced to be applied to other DHTs, for example, Kademlia.Publication Open Access Tree-stack LSTM in transition based dependency parsing(Association for Computational Linguistics (ACL), 2018) Department of Computer Engineering; N/A; YĆ¼ret, 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 Ļ-LSTM, buffer's Ī²LSTM, 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 theāKParseā 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) SahillioÄlu, Y.; Department of Computer Engineering; Yemez, YĆ¼cel; 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.