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Permanent URI for this collectionhttps://hdl.handle.net/20.500.14288/3
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Publication Metadata only Spatial and thermal aware methods for efficient workload management in distributed data centers(Elsevier B.V., 2024) N/A; Department of Computer Engineering; Ali, Ahsan; Özkasap, Öznur; Department of Computer Engineering; Graduate School of Sciences and Engineering; College of EngineeringGeographically distributed data centers provide facilities for users to fulfill the demand of storage and computations, where most of the operational cost is due to electricity consumption. In this study, we address the problem of energy consumption of cloud data centers and identify key characteristics of techniques proposed for reducing operational costs, carbon emissions, and financial penalties due to service level agreement (SLA) violations. By considering computer room air condition (CRAC) units that utilize outside air for cooling purposes as well as temperature and space-varying properties, we propose the energy cost model which takes into account temperature ranges for cooling purposes and operations of CRAC units. Then, we propose spatio-thermal-aware algorithms to manage workload using the variation of electricity price, locational outside and within the data center temperature, where the aim is to schedule the incoming workload requests with minimum SLA violations, cooling cost, and energy consumption. We analyzed the performance of our proposed algorithms and compared the experimental results with the benchmark algorithms for metrics of interest including SLA violations, cooling cost, and overall operations cost. Modeling, experiments, and verification conducted on CloudSim with realistic data center scenarios and workload traces show that the proposed algorithms result in reduced SLA violations, save between 15% to 75% of cooling cost and between 3.89% to 39% of the overall operational cost compared to the existing solutions.Publication Metadata only Virtual reality simulation-based training in otolaryngology(Springer London Ltd, 2023) N/A; Ünsaler, Selin; Hafız, Ayşenur Meriç; Gökler, Ozan; Özkaya, Yasemin Sıla; School of Medicine; Koç University HospitalVR simulators will gain wider place in medical education in order to ensure high quality surgical training. The integration of VR simulators into residency programs is actually required more than ever in the era after the pandemic. In this review, the literature is reviewed for articles that reported validation results of different VR simulators designed for the field of otolaryngology. A total of 213 articles searched from Pubmed and Web of Science databases with the key words "virtual reality simulation" and "otolaryngology" on January 2022 are retrieved. After removal of duplicates, 190 articles were reviewed by two independent authors. All the accessible articles in english and which report on validation studies of virtual reality systems are included in this review. There were 33 articles reporting validation studies of otolaryngology simulators. Twenty one articles reported on otology simulator validation studies, eight articles reported rhinology simulator validation studies and four articles reported on pharyngeal and laryngeal surgery simulators. Otology simulators are shown to increase the performance of the trainees. In some studies, efficacy of simulators has been found comparable to cadaveric bone dissections and trainees reported that VR simulators was very useful in facilitating the learning process and improved the learning curves. Rhinology simulators designed for endoscopic sinus surgery are shown to have the construct validity to differentiate the surgeons of different level of expertise. Simulators in temporal bone surgery and endoscopic sinus surgery can mimic the surgical environment and anatomy along with different surgical scenarios, thus can be more implemented in surgical training and evaluation of the trainees in the future. Currently there are no validated surgical simulators for pharyngeal and laryngeal surgery.Publication Metadata only Graph domain adaptation with localized graph signal representations(Elsevier GMBH, 2024) Pilavci, Yusuf Yigit; Guneyi, Eylem Tugce; Vural, Elif; Cengiz, Cemil; ; Graduate School of Sciences and Engineering;In this paper we propose a domain adaptation algorithm designed for graph domains. Given a source graph with many labeled nodes and a target graph with few or no labeled nodes, we aim to estimate the target labels by making use of the similarity between the characteristics of the variation of the label functions on the two graphs. Our assumption about the source and the target domains is that the local behavior of the label function, such as its spread and speed of variation on the graph, bears resemblance between the two graphs. We estimate the unknown target labels by solving an optimization problem where the label information is transferred from the source graph to the target graph based on the prior that the projections of the label functions onto localized graph bases be similar between the source and the target graphs. In order to efficiently capture the local variation of the label functions on the graphs, spectral graph wavelets are used as the graph bases. Experimentation on various data sets shows that the proposed method yields quite satisfactory classification accuracy compared to reference domain adaptation methods.Publication Metadata only DiPPI: a curated data set for drug-like molecules in protein-protein interfaces(Amer Chemical Soc, 2024) Department of Computer Engineering;Department of Chemical and Biological Engineering; Cankara, Fatma; Şenyüz, Simge; Sayın, Ahenk Zeynep; Gürsoy, Attila; Keskin, Özlem; Graduate School of Sciences and Engineering; College of EngineeringProteins interact through their interfaces, and dysfunction of protein-protein interactions (PPIs) has been associated with various diseases. Therefore, investigating the properties of the drug-modulated PPIs and interface-targeting drugs is critical. Here, we present a curated large data set for drug-like molecules in protein interfaces. We further introduce DiPPI (Drugs in Protein-Protein Interfaces), a two-module web site to facilitate the search for such molecules and their properties by exploiting our data set in drug repurposing studies. In the interface module of the web site, we present several properties, of interfaces, such as amino acid properties, hotspots, evolutionary conservation of drug-binding amino acids, and post-translational modifications of these residues. On the drug-like molecule side, we list drug-like small molecules and FDA-approved drugs from various databases and highlight those that bind to the interfaces. We further clustered the drugs based on their molecular fingerprints to confine the search for an alternative drug to a smaller space. Drug properties, including Lipinski's rules and various molecular descriptors, are also calculated and made available on the web site to guide the selection of drug molecules. Our data set contains 534,203 interfaces for 98,632 protein structures, of which 55,135 are detected to bind to a drug-like molecule. 2214 drug-like molecules are deposited on our web site, among which 335 are FDA-approved. DiPPI provides users with an easy-to-follow scheme for drug repurposing studies through its well-curated and clustered interface and drug data and is freely available at http://interactome.ku.edu.tr:8501.Publication Metadata only ProInterVal: validation of protein-protein interfaces through learned interface representations(Amer Chemical Soc, 2024) Department of Chemical and Biological Engineering;Department of Computer Engineering; Övek, Damla; Keskin, Özlem; Gürsoy, Attila; 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 EngineeringProteins are vital components of the biological world and serve a multitude of functions. They interact with other molecules through their interfaces and participate in crucial cellular processes. Disruption of these interactions can have negative effects on organisms, highlighting the importance of studying protein-protein interfaces for developing targeted therapies for diseases. Therefore, the development of a reliable method for investigating protein-protein interactions is of paramount importance. In this work, we present an approach for validating protein-protein interfaces using learned interface representations. The approach involves using a graph-based contrastive autoencoder architecture and a transformer to learn representations of protein-protein interaction interfaces from unlabeled data and then validating them through learned representations with a graph neural network. Our method achieves an accuracy of 0.91 for the test set, outperforming existing GNN-based methods. We demonstrate the effectiveness of our approach on a benchmark data set and show that it provides a promising solution for validating protein-protein interfaces.Publication Metadata only HyperE2VID: improving event-based video reconstruction via hypernetworks(IEEE-Inst Electrical Electronics Engineers Inc, 2024) Ercan, Burak; Eker, Onur; Sağlam, Canberk; Erdem, Erkut; Department of Computer Engineering; Erdem, Aykut; Department of Computer Engineering; Koç Üniversitesi İş Bankası Enfeksiyon Hastalıkları Uygulama ve Araştırma Merkezi (EHAM) / Koç University İşbank Center for Infectious Diseases (KU-IS CID); College of Engineering;Event-based cameras are becoming increasingly popular for their ability to capture high-speed motion with low latency and high dynamic range. However, generating videos from events remains challenging due to the highly sparse and varying nature of event data. To address this, in this study, we propose HyperE2VID, a dynamic neural network architecture for event-based video reconstruction. Our approach uses hypernetworks to generate per-pixel adaptive filters guided by a context fusion module that combines information from event voxel grids and previously reconstructed intensity images. We also employ a curriculum learning strategy to train the network more robustly. Our comprehensive experimental evaluations across various benchmark datasets reveal that HyperE2VID not only surpasses current state-of-the-art methods in terms of reconstruction quality but also achieves this with fewer parameters, reduced computational requirements, and accelerated inference times.Publication Metadata only AffectON: Incorporating affect into dialog generation(IEEE-Inst Electrical Electronics Engineers Inc, 2023) Bucinca, Zana; Department of Computer Engineering; Yemez, Yücel; Erzin, Engin; Sezgin, Tevfik Metin; 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 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 LuxTrack: activity inference attacks via smartphone ambient light sensors and countermeasures(IEEE-Inst Electrical Electronics Engineers Inc, 2024) Seyedkazemi, Seyedpayam; Saygın, Yücel; Department of Computer Engineering; Gürsoy, Mehmet Emre; Department of Computer Engineering; ; College of Engineering;Ambient light sensors (ALSs) are integrated into mobile devices to enable various functionalities, such as automatic adjustment of screen brightness and background color. ALSs can be used to record the light intensity in the surrounding environment without requiring permission from the user. However, this ability raises novel privacy risks. In this article, we propose LuxTrack, a side-channel privacy attack that uses the ALS of a smartphone to infer the user's activity on a nearby laptop using the light emitted from the laptop screen. To demonstrate LuxTrack, we developed an Android app that records the light intensity data from the ALS of a mobile device, and used this app to create an ALS light intensity data set in a controlled environment with real human subjects. From this data set, LuxTrack extracts a total of 187 features under six categories and trains six different machine learning models for activity inference. Experiments show that LuxTrack can achieve up to 80% accuracy in inferring the sites/apps the user is viewing on their laptop. We then propose three countermeasures against LuxTrack: 1) binning;2) smoothing;and 3) noise addition. We demonstrate that while these countermeasures are effective in reducing attack accuracy, they also yield a reduction in the accuracy of legitimate tasks (e.g., adjusting screen background color). By conducting a tradeoff analysis between the attack accuracy and legitimate task accuracy, we show that the choice of the right countermeasure and parameters can enable the reduction of attack accuracy to below 30% while only incurring 3% loss in legitimate task accuracy. © 2014 IEEE.Publication Metadata only Next generation multiple access for 6G(IEEE-Inst Electrical Electronics Engineers Inc, 2023) Fang, Fang; Liu, Yuanwei; Dhillon, Harpreet S. S.; Wu, Yiqun; Ding, Zhiguo; Department of Electrical and Electronics Engineering; Başar, Ertuğrul; Department of Electrical and Electronics Engineering; ; College of Engineering;With the standardization of 5G systems, research focus is slowly shifting towards potential designs, use cases, and performance targets for 6G systems. To meet the escalating data demands of mobile devices and to deal with the deluge of data, as well as the high-rate connectivity required by bandwidth-thirsty applications (e.g., space-air-ground-integrated-networks (SAGINs), augmented reality (AR), and virtual reality (VR), etc.), 6G networks are expected to provide substantial breakthroughs beyond the previous five generations.Publication Metadata only Edge computing in future wireless networks: a comprehensive evaluation and vision for 6G and beyond(Korean Institute of Communications and Information Sciences, 2024) Ergen, Mustafa; Saoud, Bilal; Shayea, Ibraheem; El-Saleh, Ayman A.; İnan, Feride; Tüysüz, Mehmet Fatih; Department of Electrical and Electronics Engineering; Ergen, Onur; Department of Electrical and Electronics Engineering; ; College of Engineering;Future internet aims to function as a neutral in-network storage and computation platform, essential for enabling 6G and beyond wireless use cases. Information-Centric Networking and Edge Computing are key paradigms driving this vision by offering diversified services with fast response times across heterogeneous networks. This approach requires effective coordination to dynamically utilize resources like links, storage, and computation in near real-time within a non-homogenous and distributed computing environment. Additionally, networks must be aware of resource availability and reputational information to manage unknown and partially observed dynamic systems, ensuring the desired Quality of Experience (QoE). This paper provides a comprehensive evaluation of edge computing technologies, starting with an introduction to its architectural frameworks. We examine contemporary research on essential aspects such as resource allocation, computation delegation, data administration, and network management, highlighting existing research gaps. Furthermore, we explore the synergy between edge computing and 5G, and discuss advancements in 6G that enhance solutions through edge computing. Our study emphasizes the importance of integrating edge computing in future considerations, particularly regarding sustainable energy and standards. © 2024 The Author(s)