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Permanent URI for this collectionhttps://hdl.handle.net/20.500.14288/3
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Publication Metadata only An institutional perspective: how gatekeepers on a higher education interact for the organization of access(Springer, 2023) Department of Media and Visual Arts; Yıldız, Zeynep; Subaşı, Özge; Department of Media and Visual Arts; Graduate School of Social Sciences and HumanitiesThere is growing research on how collaborative systems could support equity in shaping access for marginalized communities in different contexts. Higher education institutions are essential contexts for examining issues around equity-based organization of access for diverse populations, including people with disabilities. However, there is a shortage of research in CSCW investigating equal access in higher education settings. To address this gap, in this case study, we aim to have a closer look at how gatekeepers (people who are responsible for accessibility) in a higher education institution organize access for members with disabilities. Gatekeeping has long been discussed in disability justice to examine systemic and institutional barriers for people with disabilities. We reveal how gatekeepers interact and collaborate around existing institutional communication channels to collect access-related requests and distribute access in the higher education setting. Our data shows that existing practices come with institutional challenges hindering equity and inclusion for members with disabilities. Key issues revealed through our findings are (1) communication tools and non-shared definitions around access, (2) lack of tools for experience documentation, (3) ineffective feedback loops around access requests, (4) impact-based prioritization for access requests. We discuss how our analysis contributes to equity-oriented system design for future collaboration around organizing higher education access at the institutional level.Publication Metadata only The effect of clinical decision support systems on patients, nurses, and work environment in ICUs: a systematic review(Lippincott Williams and Wilkins, 2024) Sarıköse, Seda; Çelik, Sevilay Şenol; School of NursingThis study aimed to examine the impact of clinical decision support systems on patient outcomes, working environment outcomes, and decision-making processes in nursing. The authors conducted a systematic literature review to obtain evidence on studies about clinical decision support systems and the practices of ICU nurses. For this purpose, the authors searched 10 electronic databases, including PubMed, CINAHL, Web of Science, Scopus, Cochrane Library, Ovid MEDLINE, Science Direct, Tr-Dizin, Harman, and DergiPark. Search terms included "clinical decision support systems,""decision making,""intensive care,""nurse/nursing,""patient outcome,"and "working environment"to identify relevant studies published during the period from the year 2007 to October 2022. Our search yielded 619 articles, of which 39 met the inclusion criteria. A higher percentage of studies compared with others were descriptive (20%), conducted through a qualitative (18%), and carried out in the United States (41%). According to the results of the narrative analysis, the authors identified three main themes: "patient care outcomes,""work environment outcomes,"and the "decision-making process in nursing."Clinical decision support systems, which target practices of ICU nurses and patient care outcomes, have positive effects on outcomes and show promise in improving the quality of care;however, available studies are limited.Publication Metadata only An adaptive and diversified vehicle routing approach to reducing the security risk of cash-in-transit operations(Wiley, 2017) Bozkaya, Burçin; Department of Industrial Engineering; N/A; Salman, Fatma Sibel; Telciler, Kaan; Faculty Member; Master Student; Department of Industrial Engineering; College of Engineering; Graduate School of Sciences and Engineering; 178838; N/AWe consider the route optimization problem of transporting valuables in cash-in-transit (CIT) operations. The problem arises as a rich variant of the capacitated vehicle routing problem (CVRP) with time windows and pickup and deliveries. Due to the high-risk nature of this operation (e.g., robberies) we consider a bi-objective function where we attempt to minimize the total transportation cost and the security risk of transporting valuables along the designed routes. For risk minimization, we propose a composite risk measure that is a weighted sum of two risk components: (i) following the same or very similar routes, and (ii) visiting neighborhoods with low socioeconomic status along the routes. We also consider vehicle capacities in terms of monetary value carried as per insurance regulations. We develop an adaptive randomized bi-objective path selection algorithm that uses the composite risk measure in choosing alternative paths between origin-destination pairs over a sequence of days. We solve the rich CVRP approximately for each day with updated costs. We test our solution approach on a data set from a CIT delivery service provider and provide insights on how the routes diversify daily. Our approach generates a spectrum of solutions with costrisk trade-off to support decision making.Publication Metadata only Adaptive human force scaling via admittance control for physical human-robot interaction(IEEE Computer Soc, 2021) Aydın, Yusuf; N/A; Department of Mechanical Engineering; Al Qaysi, Yahya Mohey Hamad; Başdoğan, Çağatay; PhD Student; Faculty Member; Department of Mechanical Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 125489The goal of this article is to design an admittance controller for a robot to adaptively change its contribution to a collaborative manipulation task executed with a human partner to improve the task performance. This has been achieved by adaptive scaling of human force based on her/his movement intention while paying attention to the requirements of different task phases. In our approach, movement intentions of human are estimated from measured human force and velocity of manipulated object, and converted to a quantitative value using a fuzzy logic scheme. This value is then utilized as a variable gain in an admittance controller to adaptively adjust the contribution of robot to the task without changing the admittance time constant. We demonstrate the benefits of the proposed approach by a pHRI experiment utilizing Fitts' reaching movement task. The results of the experiment show that there is a) an optimum admittance time constant maximizing the human force amplification and b) a desirable admittance gain profile which leads to a more effective co-manipulation in terms of overall task performance.Publication Metadata only Special section on the 2011 joint symposium on computational aesthetics (CAe), non-photorealistic animation and rendering (NPAR), and sketch-based interfaces and modeling (SBIM)(Pergamon-Elsevier Science Ltd, 2012) Isenberg, Tobias; Asente, Paul; Collomosse, John; Department of Computer Engineering; Sezgin, Tevfik Metin; Faculty Member; Department of Computer Engineering; College of Engineering; 18632N/APublication Metadata only Stochastic modeling and optimization for energy management in multicore systems: a video decoding case study(IEEE-Inst Electrical Electronics Engineers Inc, 2008) Yaldız, Soner; Department of Electrical and Electronics Engineering; Department of Electrical and Electronics Engineering; Demir, Alper; Taşıran, Serdar; Faculty Member; Faculty Member; Department of Electrical and Electronics Engineering; College of Engineering; College of Engineering; 3756; N/AThis paper presents a novel stochastic modeling and optimization framework for energy minimization in multicore systems running real-time applications with tolerance to deadline misses. This framework is based on stochastic application models, which capture the variability of and the spatial and temporal correlations among the workloads of concurrent and interdependent tasks that constitute the application. These stochastic models are utilized in novel mathematical formulations to obtain optimal energy management policies. Experimental results on MPEG2 video decoding show that significant energy savings can be achieved, often close to the theoretical upper bound.Publication Metadata only The JESTKOD database: an affective multimodal database of dyadic interactions(Springer, 2017) N/A; N/A; N/A; N/A; Department of Computer Engineering; Department of Computer Engineering; Bozkurt, Elif; Khaki, Hossein; Keçeci, Sinan; Türker, Bekir Berker; Yemez, Yücel; Erzin, Engin; PhD Student; PhD Student; Master Student; PhD Student; Faculty Member; Faculty Member; Department of Computer Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; College of Engineering; College of Engineering; N/A; N/A; N/A; N/A; 107907; 34503in human-to-human communication, gesture and speech co-exist in time with a tight synchrony, and gestures are often utilized to complement or to emphasize speech. in human-computer interaction systems, natural, Affective and believable use of gestures would be a valuable key component in adopting and emphasizing human-centered aspects. However, natural and affective multimodal data, for studying computational models of gesture and speech, is limited. in this study, we introduce the JESTKOD database, which consists of speech and full-body motion capture data recordings in dyadic interaction setting under agreement and disagreement scenarios. Participants of the dyadic interactions are native Turkish speakers and recordings of each participant are rated in dimensional affect space. We present our multimodal data collection and annotation process, As well as our preliminary experimental studies on agreement/disagreement classification of dyadic interactions using body gesture and speech data. the JESTKOD database provides a valuable asset to investigate gesture and speech towards designing more natural and affective human-computer interaction systems.Publication Metadata only Per-GOP bitrate adaptation for 11.264 compressed video sequences(Springer, 2006) De Martin, J.C.; Department of Computer Engineering; N/A; Department of Electrical and Electronics Engineering; Department of Electrical and Electronics Engineering; De Vito, Fabio; Özçelebi, Tanır; Civanlar, Mehmet Reha; Tekalp, Ahmet Murat; Other; PhD Student; Faculty Member; Faculty Member; Department of Computer Engineering; Department of Electrical and Electronics Engineering; College of Engineering; Graduate School of Sciences and Engineering; College of Engineering; College of Engineering; N/A; N/A; 16372; 26207In video transmission over packet data networks, it may be desirable to adapt the coding rate according to bandwidth availability. Classical approaches to rate adaptation are bitstream switching, requiring the storage of several pre-coded versions of a video, or layered (scalable) video coding, which has coding efficiency and/or complexity penalties. In this paper we propose a new GOP-level rate adaptation scheme for a single stream, variable target bitrate H.264 encoder; this allows each group of pictures (GOP) to be encoded at a specified bitrate. We first compare the performance of the standard H.264 rate control algorithm with the proposed one in the case of constant target bitrate. Then, we present results on how close the new technique can track a specified per-GOP target bitrate schedule. Results show that the proposed approach can obtain the desired target rates with less than 5% error.Publication Metadata only FlexDPDP: flexlist-based optimized dynamic provable data possession(assoc Computing Machinery, 2016) N/A; N/A; N/A; Department of Computer Engineering; Department of Computer Engineering; Department of Computer Engineering; Esiner, Ertem; Kachkeev, Adilet; Küpçü, Alptekin; Özkasap, Öznur; Master Student; Master Student; N/A; Faculty Member; Faculty Member; Department of Computer Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; College of Engineering; College of Engineering; N/A; N/A; N/A; 168060; 113507With increasing popularity of cloud storage, efficiently proving the integrity of data stored on an untrusted server has become significant. authenticated skip lists and rank-based authenticated skip lists (RBaSL) have been used to provide support for provable data update operations in cloud storage. However, in a dynamic file scenario, An RBaSL based on block indices falls short when updates are not proportional to a fixed block size; such an update to the file, even if small, may result in O(n) updates on the data structure for a file with n blocks. To overcome this problem, we introduce FlexList, A flexible length-based authenticated skip list. FlexList translates variable-size updates to O(inverted right perpendicularu/Binverted left perpendicular) insertions, removals, or modifications, where u is the size of the update and B is the (average) block size. We further present various optimizations on the four types of skip lists (regular, Authenticated, rank-based authenticated, and FlexList). We build such a structure in O(n) time and parallelize this operation for the first time. We compute one single proof to answer multiple (non) membership queries and obtain efficiency gains of 35%, 35%, and 40% in terms of proof time, energy, and size, respectively. We propose a method of handling multiple updates at once, Achieving efficiency gains of up to 60% at the server side and 90% at the client side. We also deployed our implementation of FlexDPDP (dynamic provable data possession (DPDP) with FlexList instead of RBaSL) on PlanetLab, demonstrating that FlexDPDP performs comparable to the most efficient static storage scheme (provable data possession (PDP)) while providing dynamic data support.Publication Metadata only Recognition of haptic interaction patterns in dyadic joint object manipulation(IEEE Computer Society, 2015) KucukYılmaz, Ayse; N/A; Department of Computer Engineering; Department of Mechanical Engineering; Madan, Çığıl Ece; Sezgin, Tevfik Metin; Başdoğan, Çağatay; Master Student; Faculty Member; Faculty Member; Department of Computer Engineering; Department of Mechanical Engineering; Graduate School of Sciences and Engineering; College of Engineering; College of Engineering; N/A; 18632; 125489The development of robots that can physically cooperate with humans has attained interest in the last decades. Obviously, this effort requires a deep understanding of the intrinsic properties of interaction. Up to now, many researchers have focused on inferring human intents in terms of intermediate or terminal goals in physical tasks. On the other hand, working side by side with people, an autonomous robot additionally needs to come up with in-depth information about underlying haptic interaction patterns that are typically encountered during human-human cooperation. However, to our knowledge, no study has yet focused on characterizing such detailed information. In this sense, this work is pioneering as an effort to gain deeper understanding of interaction patterns involving two or more humans in a physical task. We present a labeled human-human-interaction dataset, which captures the interaction of two humans, who collaboratively transport an object in an haptics-enabled virtual environment. In the light of information gained by studying this dataset, we propose that the actions of cooperating partners can be examined under three interaction types: In any cooperative task, the interacting humans either 1) work in harmony, 2) cope with conflicts, or 3) remain passive during interaction. In line with this conception, we present a taxonomy of human interaction patterns; then propose five different feature sets, comprising force-, velocity-and power-related information, for the classification of these patterns. Our evaluation shows that using a multi-class support vector machine (SVM) classifier, we can accomplish a correct classification rate of 86 percent for the identification of interaction patterns, an accuracy obtained by fusing a selected set of most informative features by Minimum Redundancy Maximum Relevance (mRMR) feature selection method.