Browsing by Author "Sarcar, Sayan"

Now showing 1 - 5 of 5
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    Ability-based optimization of touchscreen interactions
    (2018-01-01) Sarcar, Sayan; Jokinen, Jussi P.P.; Oulasvirta, Antti; Wang, Zhenxin; Silpasuwanchai, Chaklam; Ren, Xiangshi; Kochi University of Technology; Department of Communications and Networking; Department of Computer Science
    Ability-based optimization is a computational approach for improving interface designs for users with sensorimotor and cognitive impairments. Designs are created by an optimizer, evaluated against task-specific cognitive models, and adapted to individual abilities. The approach does not necessitate extensive data collection and could be applied both automatically and manually by users, designers, or caretakers. As a first step, the authors present optimized touchscreen layouts for users with tremor and dyslexia that potentially improve text-entry speed and reduce error.
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    Adaptive feature guidance: Modelling visual search with graphical layouts
    (Academic Press Inc., 2020-04-01) Jokinen, Jussi P.P.; Wang, Zhenxin; Sarcar, Sayan; Oulasvirta, Antti; Ren, Xiangshi; Department of Communications and Networking; User Interfaces; Finnish Center for Artificial Intelligence, FCAI; Kochi University of Technology
    We present a computational model of visual search on graphical layouts. It assumes that the visual system is maximising expected utility when choosing where to fixate next. Three utility estimates are available for each visual search target: one by unguided perception only, and two, where perception is guided by long-term memory (location or visual feature). The system is adaptive, starting to rely more upon long-term memory when its estimates improve with experience. However, it needs to relapse back to perception-guided search if the layout changes. The model provides a tool for practitioners to evaluate how easy it is to find an item for a novice or an expert, and what happens if a layout is changed. The model suggests, for example, that (1) layouts that are visually homogeneous are harder to learn and more vulnerable to changes, (2) elements that are visually salient are easier to search and more robust to changes, and (3) moving a non-salient element far away from original location is particularly damaging. The model provided a good match with human data in a study with realistic graphical layouts.
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    Approaching Aesthetics on User Interface and Interaction Design
    (2018-11-19) Wang, Chen; Sarcar, Sayan; Kurosu, Masaaki; Bardzell, Jeffrey; Oulasvirta, Antti; Miniukovich, Aliaksei; Ren, Xiangshi; Department of Communications and Networking; Helsinki Institute for Information Technology (HIIT); User Interfaces; Kochi University of Technology; Open University of Japan; Indiana University; BEC-INFM; University of Tsukuba
    Although the HCI community inevitably contributes to engagement via beauty according to the attention paid to known and yet to be discovered principles of aesthetics for digital interface design, it is lacking an epistemological corpus which should include the notion, human factors and the quantification of aesthetic aspects. The aim of the proposed workshop is to discuss these issues in order to strengthen aesthetic studies specifically for HCI and related fields. We want to create a forum for discussing, drafting and promoting the foundations for disciplined aesthetics design within the HCI community. We thus welcome contributions such as theories, methodologies, evaluation methods, and potential applications regarding effective aesthetics for HCI and related fields. Concretely, we aim to (i) map the present state-of-art of aesthetic research in HCI, (ii) build a multidisciplinary community of experts, and (iii) raise the profile of this aesthetics research area within HCI community.
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    Approaching Engagement towards Human-Engaged Computing
    (2018-04) Salehzadeh Niksirat, Kavous; Sarcar, Sayan; Sun, Huatong; Law, Effie LC; Clemmensen, Torkil; Bardzell, Jeffrey; Oulasvirta, Antti; Silpasuwanchai, Chaklam; Light, Ann; Ren, Xiangshi; Department of Communications and Networking; Helsinki Institute for Information Technology (HIIT); User Interfaces; Kochi University of Technology; University of Washington; University of Leicester; Copenhagen Business School; Indiana University of Pennsylvania; Stamford International University; University of Sussex
    Debates regarding the nature and role of HCI research and practice have intensified in recent years, given the ever increasingly intertwined relations between humans and technologies. The framework of Human-Engaged Computing (HEC) was proposed and developed over a series of scholarly workshops to complement mainstream HCI models by leveraging synergy between humans and computers with its key notion of "engagement". Previous workshop meetings found "engagement" to be a constructive and extendable notion through which to investigate synergized human-computer relationships, but many aspects concerning the core concept remain underexplored. This SIG aims to tackle the notion of engagement considered through discussions of four thematic threads. It will bring together HCI practitioners and researchers from different disciplines including Humanities, Design, Positive Psychology, Communication and Media Studies, Neuroscience, Philosophy and Eastern Studies, to share and discuss relevant knowledge and insights and identify new research opportunities and future directions.
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    Modelling Learning of New Keyboard Layouts
    (2017) Jokinen, Jussi PP; Sarcar, Sayan; Oulasvirta, Antti; Silpasuwanchai, Chaklam; Wang, Zhenxin; Ren, Xiangshi; Department of Communications and Networking; Helsinki Institute for Information Technology (HIIT); User Interfaces; Kochi University of Technology
    Predicting how users learn new or changed interfaces is a long-standing objective in HCI research. This paper contributes to understanding of visual search and learning in text entry. With a goal of explaining variance in novices' typing performance that is attributable to visual search, a model was designed to predict how users learn to locate keys on a keyboard: initially relying on visual short-term memory but then transitioning to recall-based search. This allows predicting search times and visual search patterns for completely and partially new layouts. The model complements models of motor performance and learning in text entry by predicting change in visual search patterns over time. Practitioners can use it for estimating how long it takes to reach the desired level of performance with a given layout.