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Permanent URI for this collectionhttps://hdl.handle.net/20.500.14288/3
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Item Metadata only Designers' expectations from virtual product experience in metaverse(Association for Computing Machinery, 2023) N/A; N/A; N/A; 0000-0002-3610-4712; N/A; N/A; N/A; Department of Media and Visual Arts; Cılızoğlu, Serra; Aslan, Mehtap Duru; Ceyhan, Melisa; Yantaç, Asım Evren; PhD Student; Other; Other; Faculty Member; Graduate School of Social Sciences and Humanities; N/A; N/A; College of Social Sciences and Humanities; N/A; N/A; N/A; 52621Virtual spaces and immersive environments have been the focal point of HCI research for years. However, the Metaverse poses new challenges and opportunities for user experience in the industry and literature. One significant gap lies in understanding the expectations and perspectives of architects, product designers, and fashion designers regarding their desire to use the Metaverse for exploring new interactions with users. In this study, we explore the anticipations of 34 designers from various design fields, uncovering valuable insights into how these professionals envision integrating the Metaverse into their work and its potential impact on user engagement. Through semi-structured interviews, we identified three critical themes that are crucial to designers' engagement with the Metaverse: (1) Ambient experiences, which delve into the environmental aspects of design within the Metaverse; (2) Representation of Avatars, which examines the importance of customizable and diverse user representations, embodiment, and user interaction; and finally, (3) The design of collaborative interaction in these virtual spaces. Our study contributes to the HCI literature by providing a unique lens into designers' views of the Metaverse, reporting insights from non-Metaverse user designers, and suggest future directions.Publication Metadata only Multi-scale deformable alignment and content-adaptive inference for flexible-rate bi-directional video compression(IEEE Computer Society, 2023) Department of Electrical and Electronics Engineering; Department of Electrical and Electronics Engineering; Yılmaz, Mustafa Akın; Ulaş, Ökkeş Uğur; Tekalp, Ahmet Murat; Graduate School of Sciences and Engineering; College of EngineeringThe lack of ability to adapt the motion compensation model to video content is an important limitation of current end-to-end learned video compression models. This paper advances the state-of-the-art by proposing an adaptive motion-compensation model for end-to-end rate-distortion optimized hierarchical bi-directional video compression. In particular, we propose two novelties: i) a multi-scale deformable alignment scheme at the feature level combined with multi-scale conditional coding, ii) motion-content adaptive inference. In addition, we employ a gain unit, which enables a single model to operate at multiple rate-distortion operating points. We also exploit the gain unit to control bit allocation among intra-coded vs. bi-directionally coded frames by fine tuning corresponding models for truly flexible-rate learned video coding. Experimental results demonstrate state-of-the-art rate-distortion performance exceeding those of all prior art in learned video coding1.Publication Metadata only Artificial intelligence and medical decision-making: Wind of change for medical malpractice liability and insurance?(Edward Elgar Publishing Ltd., 2023) N/A; Çelebi, Özgün; Buğra, Ayşegül; Law SchoolIn the field of health care, computing systems' extensive capabilities and ongoing improvements as to their autonomy raise the question of whether the patient's expectations from healthcare professionals and the corresponding rules regarding assessment of medical liability will and shall remain the same. This chapter attempts to analyse the impact of cognitive computing systems - regarded neither as a human-dependent medical device nor as an autonomous and trustworthy agent - upon medical malpractice and the development of an adequate insurance system. In particular, the chapter deals with the difficulties relating to the detection of the root cause of the misdiagnosis, the impact of the use of cognitive computer systems upon the standard of care expected from health practitioners, the interactions of the systems with the requirements regarding the patient's informed consent and the role of medical liability insurance upon patient safety and compensation. © Edward Elgar Publishing 2023.Publication Metadata only Snoopie: a multi-GPU communication profiler and visualizer(Assoc Computing Machinery, 2024) Department of Computer Engineering; Department of Computer Engineering; Issa, Mohammad Kefah Taha; Sasongko, Muhammad Aditya; Turimbetov, İlyas; Baydamirli, Javid; Sağbili, Doğan; Erten, Didem Unat; Graduate School of Sciences and Engineering; College of EngineeringWith data movement becoming one of the most expensive bottlenecks in computing, the need for profiling tools to analyze communication becomes crucial for effectively scaling multi-GPU applications. While existing profiling tools including first-party software by GPU vendors are robust and excel at capturing compute operations within a single GPU, support for monitoring GPU-GPU data transfers and calls issued by communication libraries is currently inadequate. To fill these gaps, we introduce Snoopie, an instrumentation-based multi-GPU communication profiling tool built on NVBit, capable of tracking peer-to-peer transfers and GPU-centric communication library calls. To increase programmer productivity, Snoopie can attribute data movement to the source code line and the data objects involved. It comes with multiple visualization modes at varying granularities, from a coarse view of the data movement in the system as a whole to specific instructions and addresses. Our case studies demonstrate Snoopie's effectiveness in monitoring data movement, locating performance bugs in applications, and understanding concrete data transfers abstracted beneath communication libraries. The tool is publicly available at https://github.com/ParCoreLab/snoopie.Publication Metadata only Learning markerless robot-depth camera calibration and end-effector pose estimation(Ml Research Press, 2023) Department of Computer Engineering; Department of Computer Engineering; Sefercik, Buğra Can; Akgün, Barış; 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 EngineeringTraditional approaches to extrinsic calibration use fiducial markers and learning-based approaches rely heavily on simulation data. In this work, we present a learning-based markerless extrinsic calibration system that uses a depth camera and does not rely on simulation data. We learn models for end-effector (EE) segmentation, single-frame rotation prediction and keypoint detection, from automatically generated real-world data. We use a transformation trick to get EE pose estimates from rotation predictions and a matching algorithm to get EE pose estimates from keypoint predictions. We further utilize the iterative closest point algorithm, multiple-frames, filtering and outlier detection to increase calibration robustness. Our evaluations with training data from multiple camera poses and test data from previously unseen poses give sub-centimeter and sub-deciradian average calibration and pose estimation errors. We also show that a carefully selected single training pose gives comparable results. © 2023 Proceedings of Machine Learning Research. All rights reserved.Publication Metadata only Multimodal fusion for effective recommendations on a user-anonymous price comparison platform(Institute of Electrical and Electronics Engineers Inc., 2024) Kantarcı, Merve Gül; Department of Industrial Engineering; Department of Industrial Engineering; Gönen, Mehmet; College of EngineeringThis study proposes a novel recommendation framework designed for a digital price comparison platform. The challenges arise from the absence of user login and gold labels in item variations, which make effective recommendations tricky. The proposed framework integrates three distinct modalities: product titles using a multilingual BERT model, product images through the CLIP model, and click data via a novel Word2Vec model named Prod2Vec. Three fusion methods were tested to obtain a single unified representation for a given product: early, intermediate, and late fusion. Offline evaluations showcased a significant performance boost when leveraging all three modalities and employing intermediate fusion. The proposed framework achieved an impressive 92% Adjusted Rand Index clustering score at the category level. Fusion with two modalities also proved to be competitively effective, yielding scores between 87% and 88%. The framework was shown to be scalable by maintaining good performance even when we increased the number of categories up to 50. For online evaluations, we selected three representative categories and deployed the best-selected fusion method on the platform through A/B testing against a click-text encoding baseline. Our framework resulted in a significant improvement by increasing the Click-Through Rate from 1.43% to 3.17% across all categories. These findings underscore the efficacy of the proposed framework in enhancing user engagement and interaction with the platform. © 2024 IEEE.Publication Metadata only Lessons learned from a Citizen Science Project for Natural Language Processing(Association for Computational Linguistics (ACL), 2023) Klie, Jan-Christoph; Lee, Ji-Ung; Stowe, Kevin; Moosavi, Nafise Sadat; Bates, Luke; Petrak, Dominic; de Castilho, Richard Eckart; Gurevych, Iryna; Department of Computer Engineering; Department of Computer Engineering; Şahin, Gözde Gül; Koç Üniversitesi İş Bankası Yapay Zeka Uygulama ve Araştırma Merkezi (KUIS AI)/ Koç University İş Bank Artificial Intelligence Center (KUIS AI); College of EngineeringMany Natural Language Processing (NLP) systems use annotated corpora for training and evaluation. However, labeled data is often costly to obtain and scaling annotation projects is difficult, which is why annotation tasks are often outsourced to paid crowdworkers. Citizen Science is an alternative to crowdsourcing that is relatively unexplored in the context of NLP. To investigate whether and how well Citizen Science can be applied in this setting, we conduct an exploratory study into engaging different groups of volunteers in Citizen Science for NLP by re-annotating parts of a pre-existing crowdsourced dataset. Our results show that this can yield high-quality annotations and attract motivated volunteers, but also requires considering factors such as scalability, participation over time, and legal and ethical issues. We summarize lessons learned in the form of guidelines and provide our code and data to aid future work on Citizen Science. © 2023 Association for Computational Linguistics.Publication Metadata only Correlative information maximization: a biologically plausible approach to supervised deep neural networks without weight symmetry(Neural Information Processing Systems 36, 2023) Pehlevan, Cengiz; Department of Electrical and Electronics Engineering; Department of Electrical and Electronics Engineering; Bozkurt, Barışcan; Erdoğan, Alper Tunga; Koç Üniversitesi İş Bankası Yapay Zeka Uygulama ve Araştırma Merkezi (KUIS AI)/ Koç University İş Bank Artificial Intelligence Center (KUIS AI); Graduate School of Sciences and Engineering; College of EngineeringThe backpropagation algorithm has experienced remarkable success in training large-scale artificial neural networks;however, its biological plausibility has been strongly criticized, and it remains an open question whether the brain employs supervised learning mechanisms akin to it. Here, we propose correlative information maximization between layer activations as an alternative normative approach to describe the signal propagation in biological neural networks in both forward and backward directions. This new framework addresses many concerns about the biological-plausibility of conventional artificial neural networks and the backpropagation algorithm. The coordinate descent-based optimization of the corresponding objective, combined with the mean square error loss function for fitting labeled supervision data, gives rise to a neural network structure that emulates a more biologically realistic network of multi-compartment pyramidal neurons with dendritic processing and lateral inhibitory neurons. Furthermore, our approach provides a natural resolution to the weight symmetry problem between forward and backward signal propagation paths, a significant critique against the plausibility of the conventional backpropagation algorithm. This is achieved by leveraging two alternative, yet equivalent forms of the correlative mutual information objective. These alternatives intrinsically lead to forward and backward prediction networks without weight symmetry issues, providing a compelling solution to this long-standing challenge.Publication Metadata only Uncovering public attitudes: utilizing a PCA-based approach to examine anti-immigrant attitudes(Institute of Electrical and Electronics Engineers Inc., 2024) Öktem, Ubeyd; Graduate School of Social Sciences and HumanitiesThis research will observe the determinants of public attitudes towards immigrants in Turkey depending on survey questions from 2016 and 2019. The literature suggests the effects of several variables on the perception towards immigrants, with specific emphasis on encounter level and economic conditions. This research aims to test these theories in Turkey's case with migration and observe their effects in time. Although the primary intention is to examine these relations, the paper also claims to utilize unsupervised machine learning methods in variable construction/indexing. Therefore, the other aspect of this research is to utilize Principal Component Analysis in variable construction as opposed to equally weighting different variables. As a combination of quantitative and computational sociology methodologies, this paper will initially answer two forms of questions: one sociological and the other methodological. © 2024 IEEE.Publication Metadata only Implications of node selection in decentralized federated learning(IEEE, 2023) Department of Computer Engineering; Department of Computer Engineering; Lodhi, Ahnaf Hannan; Akgün, Barış; Özkasap, Öznur; Graduate School of Sciences and Engineering; College of EngineeringDecentralized Federated Learning (DFL) offers a fully distributed alternative to Federated Learning (FL). However, the lack of global information in a highly heterogeneous environment negatively impacts its performance. Node selection in FL has been suggested to improve both communication efficiency and convergence rate. In order to assess its impact on DFL performance, this work evaluates node selection using performance metrics. It also proposes and evaluates a time-varying parameterized node selection method for DFL employing validation accuracy and its per-round change. The mentioned criteria are evaluated using both hard and stochastic/soft selection on sparse networks. The results indicate that the bias associated with node selection adversely impacts performance as training progresses. Furthermore, under extreme conditions, soft selection is observed to result in higher diversity for better generalization, while a completely random selection is shown to be preferable with very limited participation.