3,255 research outputs found

    Deep Learning in Visual Computing and Signal Processing

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    Prediction based task scheduling in distributed computing

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    Nudging folks towards stronger password choices:providing certainty is the key

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    Persuading people to choose strong passwords is challenging. One way to influence password strength, as and when people are making the choice, is to tweak the choice architecture to encourage stronger choice. A variety of choice architecture manipulations i.e. “nudges”, have been trialled by researchers with a view to strengthening the overall password profile. None has made much of a difference so far. Here we report on our design of an influential behavioural intervention tailored to the password choice context: a hybrid nudge that significantly prompted stronger passwords.We carried out three longitudinal studies to analyse the efficacy of a range of “nudges” by manipulating the password choice architecture of an actual university web application. The first and second studies tested the efficacy of several simple visual framing “nudges”. Password strength did not budge. The third study tested expiration dates directly linked to password strength. This manipulation delivered a positive result: significantly longer and stronger passwords. Our main conclusion was that the final successful nudge provided participants with absolute certainty as to the benefit of a stronger password, and that it was this certainty that made the difference

    Conceptual design framework for information visualization to support multidimensional datasets in higher education institutions

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    Information Visualization (InfoVis) enjoys diverse adoption and applicability because of its strength in solving the problem of information overload inherent in institutional data. Policy and decision makers of higher education institutions (HEIs) are also experiencing information overload while interacting with students‟ data, because of its multidimensionality. This constraints decision making processes, and therefore requires a domain-specific InfoVis conceptual design framework which will birth the domain‟s InfoVis tool. This study therefore aims to design HEI Students‟ data-focused InfoVis (HSDI) conceptual design framework which addresses the content delivery techniques and the systematic processes in actualizing the domain specific InfoVis. The study involved four phases: 1) a users‟ study to investigate, elicit and prioritize the students‟ data-related explicit knowledge preferences of HEI domain policy. The corresponding students‟ data dimensions are then categorised, 2) exploratory study through content analysis of InfoVis design literatures, and subsequent mapping with findings from the users‟ study, to propose the appropriate visualization, interaction and distortion techniques for delivering the domain‟s explicit knowledge preferences, 3) conceptual development of the design framework which integrates the techniques‟ model with its design process–as identified from adaptation of software engineering and InfoVis design models, 4) evaluation of the proposed framework through expert review, prototyping, heuristics evaluation, and users‟ experience evaluation. For an InfoVis that will appropriately present and represent the domain explicit knowledge preferences, support the students‟ data multidimensionality and the decision making processes, the study found that: 1) mouse-on, mouse-on-click, mouse on-drag, drop down menu, push button, check boxes, and dynamics cursor hinting are the appropriate interaction techniques, 2) zooming, overview with details, scrolling, and exploration are the appropriate distortion techniques, and 3) line chart, scatter plot, map view, bar chart and pie chart are the appropriate visualization techniques. The theoretical support to the proposed framework suggests that dictates of preattentive processing theory, cognitive-fit theory, and normative and descriptive theories must be followed for InfoVis to aid perception, cognition and decision making respectively. This study contributes to the area of InfoVis, data-driven decision making process, and HEI students‟ data usage process

    Faculty Publications & Presentations, 2008-2009

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