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Description
There is a popular notion that creativity is highly valued in our culture. However, those "in the trenches," people in creative endeavors that actually produce the acts of creativity, say this is not so. There is a negative correlation between the value stated and the true value placed on creativity by our contemporary culture. The primary purpose of this study was to investigate that correlation as well as a possible contributing factor to this negative correlation--the fear of risk involved in enacting and accepting creativity. The methods used in this study were literature review and interview. An extensive literature review was done, as much has been written on creativity. The review was done in four parts: 1) the difficulty in defining creativity; 2) fear and the fear of creativity; 3) solutions - ways to be, express, and accept creativity; and 4) the plethora of articles written about creativity. Six one-on-one interviews were conducted with creative individuals from a variety of commercial creative endeavors. Creatives in commercial fields were chosen specifically because of their ability to influence the culture. The results of this study showed that the hypothesis, that there is a negative correlation between the value stated and the true value placed on creativity, is true. The fear of risk involved in enacting and accepting creativity as a factor in this dichotomy was also shown to be true.
ContributorsGelman, Howard P (Author) / Heywood, Wil (Thesis advisor) / Patel, Mookesh (Committee member) / Knox, Gordon (Committee member) / Arizona State University (Publisher)
Created2013
Description
Statistics is taught at every level of education, yet teachers often have to assume their students have no knowledge of statistics and start from scratch each time they set out to teach statistics. The motivation for this experimental study comes from interest in exploring educational applications of augmented reality (AR) delivered via mobile technology that could potentially provide rich, contextualized learning for understanding concepts related to statistics education. This study examined the effects of AR experiences for learning basic statistical concepts. Using a 3 x 2 research design, this study compared learning gains of 252 undergraduate and graduate students from a pre- and posttest given before and after interacting with one of three types of augmented reality experiences, a high AR experience (interacting with three dimensional images coupled with movement through a physical space), a low AR experience (interacting with three dimensional images without movement), or no AR experience (two dimensional images without movement). Two levels of collaboration (pairs and no pairs) were also included. Additionally, student perceptions toward collaboration opportunities and engagement were compared across the six treatment conditions. Other demographic information collected included the students' previous statistics experience, as well as their comfort level in using mobile devices. The moderating variables included prior knowledge (high, average, and low) as measured by the student's pretest score. Taking into account prior knowledge, students with low prior knowledge assigned to either high or low AR experience had statistically significant higher learning gains than those assigned to a no AR experience. On the other hand, the results showed no statistical significance between students assigned to work individually versus in pairs. Students assigned to both high and low AR experience perceived a statistically significant higher level of engagement than their no AR counterparts. Students with low prior knowledge benefited the most from the high AR condition in learning gains. Overall, the AR application did well for providing a hands-on experience working with statistical data. Further research on AR and its relationship to spatial cognition, situated learning, high order skill development, performance support, and other classroom applications for learning is still needed.
ContributorsConley, Quincy (Author) / Atkinson, Robert K (Thesis advisor) / Nguyen, Frank (Committee member) / Nelson, Brian C (Committee member) / Arizona State University (Publisher)
Created2013
Description
Autonomous vehicle control systems utilize real-time kinematic Global Navigation Satellite Systems (GNSS) receivers to provide a position within two-centimeter of truth. GNSS receivers utilize the satellite signal time of arrival estimates to solve for position; and multipath corrupts the time of arrival estimates with a time-varying bias. Time of arrival estimates are based upon accurate direct sequence spread spectrum (DSSS) code and carrier phase tracking. Current multipath mitigating GNSS solutions include fixed radiation pattern antennas and windowed delay-lock loop code phase discriminators. A new multipath mitigating code tracking algorithm is introduced that utilizes a non-symmetric correlation kernel to reject multipath. Independent parameters provide a means to trade-off code tracking discriminant gain against multipath mitigation performance. The algorithm performance is characterized in terms of multipath phase error bias, phase error estimation variance, tracking range, tracking ambiguity and implementation complexity. The algorithm is suitable for modernized GNSS signals including Binary Phase Shift Keyed (BPSK) and a variety of Binary Offset Keyed (BOC) signals. The algorithm compensates for unbalanced code sequences to ensure a code tracking bias does not result from the use of asymmetric correlation kernels. The algorithm does not require explicit knowledge of the propagation channel model. Design recommendations for selecting the algorithm parameters to mitigate precorrelation filter distortion are also provided.
ContributorsMiller, Steven (Author) / Spanias, Andreas (Thesis advisor) / Tepedelenlioğlu, Cihan (Committee member) / Tsakalis, Konstantinos (Committee member) / Zhang, Junshan (Committee member) / Arizona State University (Publisher)
Created2013
Description
Since its launch by the US Green Building Council (USGBC), Leadership in Energy and Environmental Design (LEED) certification has been postured as the "gold standard" for environmentally conscious, sustainable building design, construction and operations. However, as a "living measurement", one which requires ongoing evaluation and reporting of attainment and compliance with LEED certification requirements, there is none. Once awarded, LEED certification does not have a required reporting component to effectively track continued adherence to LEED standards. In addition, there is no expiry tied to the certification; once obtained, a LEED certification rating is presumed to be a valid representation of project certification status. Therefore, LEED lacks a requirement to demonstrate environmental impact of construction materials and building systems over the entire life of the project. Consequently, LEED certification is merely a label rather than a true representation of ongoing adherence to program performance requirements over time. Without continued monitoring and reporting of building design and construction features, and in the absence of recertification requirements, LEED is, in reality, a gold star rather than a gold standard. This thesis examines the lack of required ongoing monitoring, reporting, or recertification requirements following the award by the USGBC of LEED certification; compares LEED with other international programs which do have ongoing reporting or recertification requirements; demonstrates the need and benefit of ongoing reporting or recertification requirements; and explores possible methods for implementation of mandatory reporting requirements within the program.
ContributorsCarpenter, Anne Therese (Author) / Olson, Larry (Thesis advisor) / Hild, Nicholas (Committee member) / Brown, Albert (Committee member) / Arizona State University (Publisher)
Created2013
Description
Automating aspects of biocuration through biomedical information extraction could significantly impact biomedical research by enabling greater biocuration throughput and improving the feasibility of a wider scope. An important step in biomedical information extraction systems is named entity recognition (NER), where mentions of entities such as proteins and diseases are located within natural-language text and their semantic type is determined. This step is critical for later tasks in an information extraction pipeline, including normalization and relationship extraction. BANNER is a benchmark biomedical NER system using linear-chain conditional random fields and the rich feature set approach. A case study with BANNER locating genes and proteins in biomedical literature is described. The first corpus for disease NER adequate for use as training data is introduced, and employed in a case study of disease NER. The first corpus locating adverse drug reactions (ADRs) in user posts to a health-related social website is also described, and a system to locate and identify ADRs in social media text is created and evaluated. The rich feature set approach to creating NER feature sets is argued to be subject to diminishing returns, implying that additional improvements may require more sophisticated methods for creating the feature set. This motivates the first application of multivariate feature selection with filters and false discovery rate analysis to biomedical NER, resulting in a feature set at least 3 orders of magnitude smaller than the set created by the rich feature set approach. Finally, two novel approaches to NER by modeling the semantics of token sequences are introduced. The first method focuses on the sequence content by using language models to determine whether a sequence resembles entries in a lexicon of entity names or text from an unlabeled corpus more closely. The second method models the distributional semantics of token sequences, determining the similarity between a potential mention and the token sequences from the training data by analyzing the contexts where each sequence appears in a large unlabeled corpus. The second method is shown to improve the performance of BANNER on multiple data sets.
ContributorsLeaman, James Robert (Author) / Gonzalez, Graciela (Thesis advisor) / Baral, Chitta (Thesis advisor) / Cohen, Kevin B (Committee member) / Liu, Huan (Committee member) / Ye, Jieping (Committee member) / Arizona State University (Publisher)
Created2013
Description
Objective: The aim of this research is to uncover, via a comprehensive cross study analysis, data patterns that could potentially point to a positive correlation between two main variables: anesthetic monitoring equipment and anesthetic decision making. Of particular interest is the equipment's monitor screen and the extent to which its user interface design influences anesthetic situation awareness (SA) and hence, decision making. It is hypothesized that poor anesthetic diagnosis from inadequate SA may be largely attributable to patient data displays lacking in human factors design considerations. Methods: A systematic search was conducted of existing empirical studies pertaining to patient physiologic monitoring that spanned across interrelated domains, namely, ergonomics, medical informatics, visual computing, cognitive psychology, human factors, clinical monitoring, intensive care medicine, and intelligent systems etc. all published in scholarly research journals between 1970 to August 2012. Anesthetic-related keywords were queried i.e. anesthetic mishaps, patient physiological data displays, anesthetic vigilance etc. (found in Appendix A). This approach yielded a few thousand results, of which 65 empirical studies were pulled. Further extraction of articles having direct connection to the use of data displays within the anesthetic context produced a total of 20 empirical studies. These studies were grouped under two broad categories of Monitoring and Monitors whereby factors directly contributing to the studies' results were identified with the aim to find emerging themes that provide insights involving interface design and medical decision making. Results: There is a direct correlation between user-interface design and decision making. The situation awareness (SA) required for decision making heavily relies upon data displays oriented towards information extraction and integration. In the systematic assessment of empirical studies, it is undeniable how strikingly prominent visual attributes show up as contributing factors to subjects' enhanced performance in the studies. Conclusions: How and to what users direct their perceptual and cognitive resources necessarily influence their perception of the environment, and by extension, their development of situation awareness (SA). Although patient monitoring equipment employed in anesthetic practice has proven to be indispensable in quality patient care, graphical representations of patient data is still far from optimal in the clinical setting. User-interfaces that lend decision support to facilitate SA and subsequent decision making is critical in crisis management.
ContributorsNguyen, Angie (Author) / Velasquez, Joseph (Thesis advisor) / McDermott, Lauren (Thesis advisor) / Herring, Don (Committee member) / Branaghan, Russell (Committee member) / Arizona State University (Publisher)
Created2013
Description
The research presented explores traditional Chinese visual design elements with a goal of incorporating them into contemporary design. It seeks to provide insight into how Chinese and non-Chinese designers and non-designers recognize common visual design elements as being associated with Chinese design. As a result, the research explores three characteristics: a) handicraft; b) naturalism; and c) design with meaning, which can be key points in understanding traditional Chinese design. Furthermore, the research explores two sets of design criteria that can guide designers to apply these representative design elements into contemporary design in order to express Chinese culture.
ContributorsRen, Liqi (Author) / Giard, Jacques (Thesis advisor) / Brown, Claudia (Committee member) / Cheung, Patrick (Committee member) / Arizona State University (Publisher)
Created2013
Description
The process of this study involves conducting empirical tests on consumer's emotional responses toward tableware designs by statistic measurements (PrEmo), including both Chinese and American cultures. The objective to this study is to research the correlation between consumers' cognitive analysis of Chinese tableware designs and their emotional responses. The author proposes that the correlationship between consumers' cognition of Chinese tableware and emotional responses will lead to a new opportunity in the industrial design industry. Fifty-seven people responded to sixty-seven invitations to join the research project at Chinese restaurants in both China and America. Throughout the process of coding and organizing the survey data, a finding shows that there is a connection between consumer sensitivity toward the products and their emotional bonds to the assigned product designs. The data showed that more people in China are expending greater effort in choosing suitable tableware designs compared to the people in the U.S. Key words: Emotion, Cognition, Culture, Tableware design, Chinese restaurants
ContributorsLiu, Ran (Author) / Herring, Donald (Thesis advisor) / Wolf, Peter (Committee member) / Wang, Ning (Committee member) / Arizona State University (Publisher)
Created2013
Description
It is commonly known that High Performance Computing (HPC) systems are most frequently used by multiple users for batch job, parallel computations. Less well known, however, are the numerous HPC systems servicing data so sensitive that administrators enforce either a) sequential job processing - only one job at a time on the entire system, or b) physical separation - devoting an entire HPC system to a single project until recommissioned. The driving forces behind this type of security are numerous but share the common origin of data so sensitive that measures above and beyond industry standard are used to ensure information security. This paper presents a network security solution that provides information security above and beyond industry standard, yet still enabling multi-user computations on the system. This paper's main contribution is a mechanism designed to enforce high level time division multiplexing of network access (Time Division Multiple Access, or TDMA) according to security groups. By dividing network access into time windows, interactions between applications over the network can be prevented in an easily verifiable way.
ContributorsFerguson, Joshua (Author) / Gupta, Sandeep Ks (Thesis advisor) / Varsamopoulos, Georgios (Committee member) / Ball, George (Committee member) / Arizona State University (Publisher)
Created2013
Description
In recent years, machine learning and data mining technologies have received growing attention in several areas such as recommendation systems, natural language processing, speech and handwriting recognition, image processing and biomedical domain. Many of these applications which deal with physiological and biomedical data require person specific or person adaptive systems. The greatest challenge in developing such systems is the subject-dependent data variations or subject-based variability in physiological and biomedical data, which leads to difference in data distributions making the task of modeling these data, using traditional machine learning algorithms, complex and challenging. As a result, despite the wide application of machine learning, efficient deployment of its principles to model real-world data is still a challenge. This dissertation addresses the problem of subject based variability in physiological and biomedical data and proposes person adaptive prediction models based on novel transfer and active learning algorithms, an emerging field in machine learning. One of the significant contributions of this dissertation is a person adaptive method, for early detection of muscle fatigue using Surface Electromyogram signals, based on a new multi-source transfer learning algorithm. This dissertation also proposes a subject-independent algorithm for grading the progression of muscle fatigue from 0 to 1 level in a test subject, during isometric or dynamic contractions, at real-time. Besides subject based variability, biomedical image data also varies due to variations in their imaging techniques, leading to distribution differences between the image databases. Hence a classifier learned on one database may perform poorly on the other database. Another significant contribution of this dissertation has been the design and development of an efficient biomedical image data annotation framework, based on a novel combination of transfer learning and a new batch-mode active learning method, capable of addressing the distribution differences across databases. The methodologies developed in this dissertation are relevant and applicable to a large set of computing problems where there is a high variation of data between subjects or sources, such as face detection, pose detection and speech recognition. From a broader perspective, these frameworks can be viewed as a first step towards design of automated adaptive systems for real world data.
ContributorsChattopadhyay, Rita (Author) / Panchanathan, Sethuraman (Thesis advisor) / Ye, Jieping (Thesis advisor) / Li, Baoxin (Committee member) / Santello, Marco (Committee member) / Arizona State University (Publisher)
Created2013