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Permanent URI for this collectionhttps://hdl.handle.net/20.500.14288/3
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Publication Metadata only AffectON: Incorporating affect into dialog generation(IEEE-Inst Electrical Electronics Engineers Inc, 2023) Bucinca, Zana; Department of Computer Engineering; Department of Computer Engineering; Yemez, Yücel; Erzin, Engin; Sezgin, Tevfik Metin; Koç Üniversitesi İş Bankası Yapay Zeka Uygulama ve Araştırma Merkezi (KUIS AI)/ Koç University İş Bank Artificial Intelligence Center (KUIS AI); College of EngineeringDue to its expressivity, natural language is paramount for explicit and implicit affective state communication among humans. The same linguistic inquiry (e.g., How are you?) might induce responses with different affects depending on the affective state of the conversational partner(s) and the context of the conversation. Yet, most dialog systems do not consider affect as constitutive aspect of response generation. In this article, we introduce AffectON, an approach for generating affective responses during inference. For generating language in a targeted affect, our approach leverages a probabilistic language model and an affective space. AffectON is language model agnostic, since it can work with probabilities generated by any language model (e.g., sequence-to-sequence models, neural language models, n-grams). Hence, it can be employed for both affective dialog and affective language generation. We experimented with affective dialog generation and evaluated the generated text objectively and subjectively. For the subjective part of the evaluation, we designed a custom user interface for rating and provided recommendations for the design of such interfaces. The results, both subjective and objective demonstrate that our approach is successful in pulling the generated language toward the targeted affect, with little sacrifice in syntactic coherence.Publication Metadata only Exploring users interested in 3D food printing and their attitudes: case of the employees of a kitchen appliance company(Taylor and Francis inc, 2022) N/A; N/A; Department of Sociology; Department of Media and Visual Arts; Department of Sociology; Department of Media and Visual Arts; Kocaman, Yağmur; Mert, Aslı Ermiş; Özcan, Oğuzhan; PhD Student; Faculty Member; Faculty Member; KU Arçelik Research Center for Creative Industries (KUAR) / KU Arçelik Yaratıcı Endüstriler Uygulama ve Araştırma Merkezi (KUAR); Graduate School of Social Sciences and Humanities; College of Social Sciences and Humanities; College of Social Sciences and Humanities; N/A; N/A; 125323D Food Printing (3DFP) technology is expected to enter homes in the near future as a kitchen appliance. on the other hand, 3DFP is perceived as a non-domestic technology by potential users and domestic users' attitudes and everyday habits received less attention in previous 3DFP research. Exploring their perspective is needed to reflect their daily kitchen dynamics on the design process and discover possible new benefits situated in the home kitchen. on this basis, this study focuses on finding potential 3DFP users and explores their attitudes towards using 3DFP technology in their home kitchens through a two-stage study: First, we prioritized potential users based on their relationship with food through a questionnaire and found six factors that positively affect their attitude towards 3DFP: cooking every day, ordering food less than once a month, eating out at least a couple of times a month, having a mini oven, A multicooker, or a kettle, liking to try new foods, thinking that cooking is a fun activity. Second, we conducted semi-structured interviews with seven participants to discuss the possible benefits and drawbacks of 3DFP technology for their daily lives in the kitchen. Results revealed two new benefits that 3DFP at home may provide: risk-free cooking and cooking for self-improvement. We discuss the potential implications of these two benefits for design and HCI research focusing on how to facilitate automation and pleasurable aspects of cooking into future 3DFP devices.Publication Metadata only Gaze-based prediction of pen-based virtual interaction tasks(Academic Press Ltd- Elsevier Science Ltd, 2015) Department of Computer Engineering; Department of Computer Engineering; Department of Computer Engineering; Çiğ, Çağla; Sezgin, Tevfik Metin; PhD Student; Faculty Member; College of Engineering; College of Engineering; N/A; 18632In typical human-computer interaction, users convey their intentions through traditional input devices (e.g. keyboards, mice, joysticks) coupled with standard graphical user interface elements. Recently, pen-based interaction has emerged as a more intuitive alternative to these traditional means. However, existing pen-based systems are limited by the fact that they rely heavily on auxiliary mode switching mechanisms during interaction (e.g. hard or soft modifier keys, buttons, menus). In this paper, we describe how eye gaze movements that naturally occur during pen-based interaction can be used to reduce dependency on explicit mode selection mechanisms in pen-based systems. In particular, we show that a range of virtual manipulation commands, that would otherwise require auxiliary mode switching elements, can be issued with an 88% success rate with the aid of users' natural eye gaze behavior during pen-only interaction. (C) 2014 Elsevier Ltd. All rights reserved.Publication Metadata only Sentiment and context-refined word embeddings for sentiment analysis(IEEE, 2021) Deniz, Ayca; Angin, Pelin; Department of International Relations; Department of International Relations; Angın, Merih; Faculty Member; College of Administrative Sciences and Economics; 308500Word embeddings have become the de-facto tool for representing text in natural language processing (NLP) tasks, as they can capture semantic and syntactic relations, unlike their precedents such as Bag-of-Words. Although word embeddings have been employed in various studies in recent years and proven to be effective in many NLP tasks, they are still immature for sentiment analysis, as they suffer from insufficient sentiment information. General word embedding models pre-trained on large corpora with methods such as Word2Vec or GloVe achieve limited success in domain-specific NLP tasks. On the other hand, training domain-specific word embeddings from scratch requires a high amount of data and computation power. In this work, we target both shortcomings of pre-trained word embeddings to boost the performance of domain-specific sentiment analysis tasks. We propose a model that refines pre-trained word embeddings with context information and leverages the sentiment scores of sentences obtained from a lexicon-based method to further improve performance. Experiment results on two benchmark datasets show that the proposed method significantly increases the accuracy of sentiment classification.Publication Metadata only Modeling sliding friction between human finger and touchscreen under electroadhesion(Ieee Computer Soc, 2020) Department of Mechanical Engineering; N/A; Department of Mechanical Engineering; Başdoğan, Çağatay; Alipour, Mohammad; Şirin, Ömer; Faculty Member; PhD Student; PhD Student; College of Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; 125489; N/A; N/AWhen an alternating voltage is applied to the conductive layer of a capacitive touchscreen, an oscillating electroadhesive force (also known as electrovibration) is generated between the human finger and its surface in the normal direction. This electroadhesive force causes an increase in friction between the sliding finger and the touchscreen. Although the practical implementation of this technology is quite straightforward, the physics behind voltage-induced electroadhesion and the resulting contact interactions between human finger and the touchscreen are still under investigation. In this article, we first present the results of our experimental study conducted with a custom-made tribometer to investigate the effect of input voltage on the tangential forces acting on the finger due to electroadhesion during sliding. We then support our experimental results with a contact mechanics model developed for estimating voltage-induced frictional forces between human finger and a touchscreen as a function of the applied normal force. The unknown parameters of the model were estimated via optimization by minimizing the error between the measured tangential forces and the ones generated by the model. The estimated model parameters show a good agreement with the ones reported in the literature.