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Publication Open Access Augmented tabletop role-playing game with movement-based gameplay and arm-worn devices(Association for Computing Machinery (ACM), 2017) Department of Computer Engineering; Buruk, Oğuz Turan; Özcan, Oğuzhan; Özbeyli, İsmet Melih; Faculty Member; Department of Computer Engineering; KU Arçelik Research Center for Creative Industries (KUAR) / KU Arçelik Yaratıcı Endüstriler Uygulama ve Araştırma Merkezi (KUAR); College of Engineering; N/A; 12532; N/AAugmenting table-top role-playing games (TTRPG) is a trending subject in game research. Different objects and interaction modalities such as surface displays, tangible devices or interactive rooms are used for the augmentation of TTRPG. Still, usage of wearable devices and movement-based gameplay in such games is a rather underexplored area although they have a potential for enhancing the player experience according to the previous studies. To delve into this area, we developed a new interactive environment comprised of arm-worn devices and an augmented die. These devices, together with a new role-playing game system, facilitate movement-based gameplay which encourage players to enact their characters with their bodies. In this paper, we explained the specifications of this gaming environment and our demonstration setting.Publication Open Access Characterizing user behavior for speech and sketch-based video retrieval interfaces(Association for Computing Machinery (ACM), 2017) Department of Computer Engineering; Sezgin, Tevfik Metin; Altıok, Ozan Can; Faculty Member; Master Student; Department of Computer Engineering; College of Engineering; 18632; N/AFrom a user interaction perspective, speech and sketching make a good couple for describing motion. Speech allows easy specification of content, events and relationships, while sketching brings in spatial expressiveness. Yet, we have insufficient knowledge of how sketching and speech can be used for motion-based video retrieval, because there are no existing retrieval systems that support such interaction. In this paper, we describe a Wizard-of-Oz protocol and a set of tools that we have developed to engage users in a sketch-and speech-based video retrieval task. We report how the tools and the protocol fit together using "retrieval of soccer videos" as a use case scenario. Our software is highly customizable, and our protocol is easy to follow. We believe that together they will serve as a convenient and powerful duo for studying a wide range of multi-modal use cases.Publication Open Access GestAnalytics: experiment and analysis tool for gesture-elicitation studies(Association for Computing Machinery (ACM), 2017) Department of Computer Engineering; Buruk, Oğuz Turan; Özcan, Oğuzhan; Faculty Member; Department of Computer Engineering; KU Arçelik Research Center for Creative Industries (KUAR) / KU Arçelik Yaratıcı Endüstriler Uygulama ve Araştırma 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 “GestAnalytics” 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 Restricted Learning markerless robot-depth camera calibration and end-effector pose estimation(Koç University, 2022) Sefercik, Buğra Can; Akgün, Barış; 0000-0002-4079-6889; Koç University Graduate School of Sciences and Engineering; Computer Science and Engineering; 258784Publication Open Access Parsing with context embeddings(Association for Computational Linguistics (ACL), 2017) Department of Computer Engineering; Yüret, Deniz; Önder, Berkay Furkan; Kırnap, Ömer; Faculty Member; Department of Computer Engineering; College of Engineering; Graduate School of Sciences and Engineering; 179996; N/A; N/AWe introduce context embeddings, dense vectors derived from a language model that represent the left/right context of a word instance, and demonstrate that context embeddings significantly improve the accuracy of our transition based parser. Our model consists of a bidirectional LSTM (BiLSTM) based language model that is pre-trained to predict words in plain text, and a multi-layer perceptron (MLP) decision model that uses features from the language model to predict the correct actions for an ArcHybrid transition based parser. We participated in the CoNLL 2017 UD Shared Task as the “Koç University” team and our system was ranked 7th out of 33 systems that parsed 81 treebanks in 49 languages.Publication Open Access Single password authentication(Elsevier, 2013) Acar, Tolga; Belenkiy, Mira; Department of Computer Engineering; Küpçü, Alptekin; Faculty Member; Department of Computer Engineering; College of Engineering; 168060Users frequently reuse their passwords when authenticating to various online services. Combined with the use of weak passwords or honeypot/phishing attacks, this brings high risks to the security of the user’s account information. In this paper, we propose several protocols that can allow a user to use a single password to authenticate to multiple services securely. All our constructions provably protect the user from dictionary attacks on the password, and cross-site impersonation or honeypot attacks by the online service providers. Our solutions assume the user has access to either an untrusted online cloud storage service (as per Boyen [16]), or a mobile storage device that is trusted until stolen. In the cloud storage scenario, we consider schemes that optimize for either storage server or online service performance, as well as anonymity and unlinkability of the user’s actions. In the mobile storage scenario, we minimize the assumptions we make about the capabilities of the mobile device: we donotassume synchronization, tamper resistance, special or expensive hardware, or extensive cryptographic capabilities. Most importantly, the user’s password remains secure even after the mobile device is stolen. Our protocols provide another layer of security against malware and phishing. To the best of our knowledge, we are the first to propose such various and provably secure password-based authentication schemes. Lastly, we argue that our constructions are relatively easy to deploy, especially if a few single sign-on services (e.g., Microsoft, Google, and Facebook) adopt our proposal.Publication Open Access Sparse: Koç University graph-based parsing system for the CoNLL 2018 shared task(Association for Computational Linguistics (ACL), 2018) Department of Computer Engineering; N/A; Yüret, Deniz; Önder, Berkay Furkan; Gümeli, Can; Faculty Member; Department of Computer Engineering; College of Engineering; Graduate School of Sciences and Engineering; 179996; N/A; N/AWe present SParse, our Graph-Based Parsing model submitted for the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies (Zeman et al., 2018). Our model extends the state-of-the-art biaffine parser (Dozat and Manning, 2016) with a structural meta-learning module, SMeta, that combines local and global label predictions. Our parser has been trained and run on Universal Dependencies datasets (Nivre et al., 2016, 2018) and has 87.48% LAS, 78.63% MLAS, 78.69% BLEX and 81.76% CLAS (Nivre and Fang, 2017) score on the Italian-ISDT dataset and has 72.78% LAS, 59.10% MLAS, 61.38% BLEX and 61.72% CLAS score on the Japanese-GSD dataset in our official submission. All other corpora are evaluated after the submission deadline, for whom we present our unofficial test results.Publication Open Access Transfer learning for low-resource neural machine translation(Association for Computational Linguistics (ACL), 2016) Zoph, Barret; May, Jonathan; Knight, Kevin; Department of Computer Engineering; Yüret, Deniz; Faculty Member; Department of Computer Engineering; College of Engineering; 179996The encoder-decoder framework for neural machine translation (NMT) has been shown effective in large data scenarios, but is much less effective for low-resource languages. We present a transfer learning method that significantly improves BLEU scores across a range of low-resource languages. Our key idea is to first train a high-resource language pair (the parent model), then transfer some of the learned parameters to the low-resource pair (the child model) to initialize and constrain training. Using our transfer learning method we improve baseline NMT models by an average of 5.6 BLEU on four low-resource language pairs. Ensembling and unknown word replacement add another 2 BLEU which brings the NMT performance on low-resource machine translation close to a strong syntax based machine translation (SBMT) system, exceeding its performance on one language pair. Additionally, using the transfer learning model for re-scoring, we can improve the SBMT system by an average of 1.3 BLEU, improving the state-of-the-art on low-resource machine translation.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 Viewpoint: AI as author - bridging the gap between machine learning and literary theory(AI Access Foundation, 2021) Baş, Anıl; Department of Comparative Literature; van Heerden, Imke; Other; Department of Comparative Literature; College of Social Sciences and Humanities; 318142Anticipating the rise in Artificial Intelligence's ability to produce original works of literature, this study suggests that literariness, or that which constitutes a text as literary, is understudied in relation to text generation. From a computational perspective, literature is particularly challenging because it typically employs figurative and ambiguous language. Literary expertise would be beneficial to understanding how meaning and emotion are conveyed in this art form but is often overlooked. We propose placing experts from two dissimilar disciplines -machine learning and literary studies- in conversation to improve the quality of AI writing. Concentrating on evaluation as a vital stage in the text generation process, the study demonstrates that benefit could be derived from literary theoretical perspectives. This knowledge would improve algorithm design and enable a deeper understanding of how AI learns and generates.