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<p>The set of related products is referred to as a product family, and feature-modeling is a widely used technique to capture the commonalities and variabilities of a product family in terms of "features". With the growing complexity of software product families in several software industries, the development, maintenance and evolution of complex and large feature models are among the main challenges faced by feature-modeling practitioners. In particular, more sophisticated feature-modeling techniques are required to address the problems caused by unanticipated changes and crosscutting concerns in feature models.</p> <p>This thesis tackles the above challenges in feature-modeling by adopting the aspect-oriented paradigm at the feature-modeling level. I first introduce a specification language, called AO-PFA, which is an extension of the Product Family Algebra (PFA) language. I then proposed a formal verification technique to check the compatibility of aspects with their base specifications in AO-PFA. In the aspect-oriented paradigm, the process of combining aspects with base specifications is referred to as the weaving process. I finally discussed how to perform the weaving process in AO-PFA. By proposing a systematic approach to extend product family algebra with the abilities of specifying, verifying, and weaving aspects, we are able to handle the difficulties that arise from crosscutting concerns and unanticipated changes in large-scale feature models.</p> / Doctor of Philosophy (PhD)

Three issues related to the prediction of aesthetic response of natural landscapes were investigated. First, information regarding the degree of correspondence between two conceptually different yet commonly used criteria of aesthetic response--ratings of scenic quality and preference ratings--was sought. Second, the relative efficiency of and interrelationships between predictor variables stemming from different philosophical paradigms of landscape aesthetics was of interest. Examination of such a variety of predictors towards the same criteria utilizing the same settings as stimuli had not been previously researched. Direct comparison of types to one another, and in combination as predictors, would indicate both whether different approaches were measuring similar aesthetic response variance, and in what ways they differed. Third, the extent to which a motivational choice model based in expectancy theory could predict environmental preference was of interest. This model represented an aesthetic predictor in terms of environmental utility, i.e., meaningfulness within the context of potential activity, and was thus a departure from traditional predictors based on design elements and the arrangement of physical features. Data were gathered from a total 354 subjects responding to 60 different natural landscape scenes (color slides) from a wide variety of United States' biomes. Results indicated that the two aesthetic criteria were nearly identical, both in relation to one another (r=.98) and through their correlate patterns to 33 predictor variables. Predictor variables from three paradigms: the psychophysical (physical features of the environment), the cognitive (transactional variables involving interpretive patterning of physical variables), and the experiential (environmental utility in terms of potential for activity) were all highly effective. Multiple regression equations for specific types had predicted R-Squares ranging from .47 to .84. In turn, detailed analyses of the transactional and utility variables via multiple regression (using the physical variables as predictors) indicated they could be defined by these managerially controllable terms. Finally, the environmental utility variable was examined in more detail through a variety of expectancy models. Of major interest was that environmental familiarity was a strong moderator of the utility effect, with highly familiar settings yielding more accurate prediction than unfamiliar settings. A number of managerial implications and suggestions for follow-up research are made. / Ph. D. / incomplete_metadata

<p dir="ltr">When diagnosed with cancer, it is not uncommon for patients to turn to narratives—both fiction and non—looking for comfort or a way to make sense of their situation. When it comes to cancer on screen, we often see a romanticized version of cancer diagnosis: young sick kids falling in love, messages of going on to do amazing things after treatment, or visuals of glamorized sickness. This is not reflective of the dark thoughts that often find homes in the minds of cancer patients. And yet, little media exists to resonate with these darker narratives. Netflix’s The Midnight Club, a horror show catered toward young adults, helps to twist the pre-existing narratives surrounding cancer by utilizing the conventions of the horror genre to explore the darker sides of cancer diagnosis through storytelling. Though often uncomfortable, the show’s ability to discuss thoughts of mortality, pain, and loss in wake of terminal diagnosis is one important of discussion, as is done in this paper.</p>

Visualization has proven to be an effective means for analyzing high-dimensional data, especially Multivariate Multidimensional (MVMD) scientific data. Scientific visualization deals with data that have natural spatial mapping such as maps, buildings interiors or even your physiological body parts, while information visualization involves abstract, non-spatial data. Visual analytics uses either visualization types to gain deep inferences about scientific data or information. In recent years, a variety of techniques have been developed combining statistical and visual analysis tools to represent data of different types in one view to enable data fusion. One vital feature of such visualization tools is the support for comparison: showing the differences between two or more objects. This feature is called visual differencing, or discrimination. Visual differencing is a common requirement across different research domains, helping analysts compare different objects in the data set or compare different attributes of the same object. From a visual analytic point of view, this research examines humans' predictable bias in interpreting visual-spatial, spatiotemporal information, and inference-making in scientific visualization. Practically, I examined different case studies from different domains such as land suitability in agriculture, spectrum sensing in software-defined radio networks, raster images in remote sensing, pattern recognition in point cloud, airflow distribution in aerodynamics, galaxy catalogs in astrophysics and protein membrane interaction in molecular dynamics. Each case required different computing power, ranging from personal computer to high performance cluster. Based on this experience across application domains, I propose a high-performance visualization paradigm for scientific visualization that supports three key features of scientific data analysis: representations, fusion, and visual discrimination. This paradigm is informed by practical work with multiple high-performance computing and visualization platforms from desktop displays to immersive CAVE displays. In order to evaluate the applicability of the proposed paradigm, I carried out two user studies. The first user study addressed the feature of data fusion with multivariate maps and the second one addressed visual differencing with three multi-view management techniques. The high-performance visualization paradigm and the results of these studies contribute to our knowledge of efficient MVMD designs and provides scientific visualization developers with a framework to mitigate the trade-offs of scalable visualization design such as the data mappings, computing power, and output modality. / Ph. D.