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Advancements in computer vision and machine learning have added a new dimension to remote sensing applications with the aid of imagery analysis techniques. Applications such as autonomous navigation and terrain classification which make use of image classification techniques are challenging problems and research is still being carried out to find

Advancements in computer vision and machine learning have added a new dimension to remote sensing applications with the aid of imagery analysis techniques. Applications such as autonomous navigation and terrain classification which make use of image classification techniques are challenging problems and research is still being carried out to find better solutions. In this thesis, a novel method is proposed which uses image registration techniques to provide better image classification. This method reduces the error rate of classification by performing image registration of the images with the previously obtained images before performing classification. The motivation behind this is the fact that images that are obtained in the same region which need to be classified will not differ significantly in characteristics. Hence, registration will provide an image that matches closer to the previously obtained image, thus providing better classification. To illustrate that the proposed method works, naïve Bayes and iterative closest point (ICP) algorithms are used for the image classification and registration stages respectively. This implementation was tested extensively in simulation using synthetic images and using a real life data set called the Defense Advanced Research Project Agency (DARPA) Learning Applied to Ground Robots (LAGR) dataset. The results show that the ICP algorithm does help in better classification with Naïve Bayes by reducing the error rate by an average of about 10% in the synthetic data and by about 7% on the actual datasets used.
ContributorsMuralidhar, Ashwini (Author) / Saripalli, Srikanth (Thesis advisor) / Papandreou-Suppappola, Antonia (Committee member) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2011
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Humans use emotions to communicate social cues to our peers on a daily basis. Are we able to identify context from facial expressions and match them to specific scenarios? This experiment found that people can effectively distinguish negative and positive emotions from each other from a short description. However, further

Humans use emotions to communicate social cues to our peers on a daily basis. Are we able to identify context from facial expressions and match them to specific scenarios? This experiment found that people can effectively distinguish negative and positive emotions from each other from a short description. However, further research is needed to find out whether humans can learn to perceive emotions only from contextual explanations.

ContributorsCulbert, Bailie (Author) / Hartwell, Leland (Thesis director) / McAvoy, Mary (Committee member) / School of Life Sciences (Contributor) / School of Criminology and Criminal Justice (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
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Since the inception of what is now known as the Behavioral Analysis Unit (BAU) at the Federal Bureau of Investigation (FBI) in the 1970s, criminal profiling has become an increasingly prevalent entity in both forensic science and the popular imagination. The fundamental idea of which profiling is premised – behavior

Since the inception of what is now known as the Behavioral Analysis Unit (BAU) at the Federal Bureau of Investigation (FBI) in the 1970s, criminal profiling has become an increasingly prevalent entity in both forensic science and the popular imagination. The fundamental idea of which profiling is premised – behavior as a reflection of personality – has been the subject of a great deal of misunderstanding, with professionals and nonprofessionals alike questioning whether profiling represents an art or a science and what its function in forensic science should be. To provide a more thorough understanding of criminal profiling’s capabilities and its efficacy as a law enforcement tool, this thesis will examine the application of criminal profiling to investigations, various court rulings concerning profiling’s admissibility, and the role that popular media plays in the perception and function of the practice. It will also discuss how future research and regulatory advancements may strengthen criminal profiling’s scientific merit and legitimacy.

ContributorsGeraghty, Bridget Elizabeth (Author) / Kobojek, Kimberly (Thesis director) / Gruber, Diane (Committee member) / School of International Letters and Cultures (Contributor) / Department of Psychology (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description

COVID-19 has shocked the bedrock of society, impacting both human life and the economy. Accompanying this shock has been the psychological distress inflicted onto the general population as a result of the emotion strain stemming from isolation/quarantine policies, being sick with COVID-19, dealing with COVID-19 losses, and post-COVID syndrome and

COVID-19 has shocked the bedrock of society, impacting both human life and the economy. Accompanying this shock has been the psychological distress inflicted onto the general population as a result of the emotion strain stemming from isolation/quarantine policies, being sick with COVID-19, dealing with COVID-19 losses, and post-COVID syndrome and its effect on quality of life. The psychological distress has been experienced by the general population, but compared to middle age (30-50) and older adults (>50 years of age), it has been young adults (18-30 years old) who have been more psychologically affected (Glowacz & Schmits, 2020). Psychological distress, specifically anxiety and depression, has been exacerbated by feelings of uncertainty, fear of illness, losing loved ones, and fear of post-COVID syndrome. Post-COVID syndrome, as with other post-viral syndromes such as post viral SARS involve lingering symptoms such as myalgic encephalomyelitis or Chronic Fatigue Syndrome (CFS), and loss of motivation (Underhill, 2015). In addition to these symptoms, patients suffering from post-COVID syndrome have also presented brain inflammation and damaged brain blood vessels (Meinhardt et al., 2021), Endotheliitis (Varga et al., 2020), CV abnormalities and changes in glucose metabolism (Williams et al., 2020). CV abnormalities and changes in glucose metabolism are connected to chronic illnesses like diabetes and heart disease respectively. These chronic illnesses are then associated with higher risk for depression as a result of the stress induced by the symptoms and their impact on quality of life (NIMH, 2021). Further monitoring, and research will be important to gauge ultimate physiological and psychological impact of COVID-19.

ContributorsPiedra Gonzalez, Michael (Author) / Vargas, Perla (Thesis director) / Oh, Hyunsung (Committee member) / College of Health Solutions (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
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This paper explores the psychological experiences of domestic workers in three contemporary Latin American films: Roma (Mexico, 2018), Crímenes de familia (Argentina, 2020) and Que Horas Ela Volta? (Brazil, 2015). Specifically, the motherhood of these three protagonists is explored and analyzed using psychological research that pertains to motherhood, trauma, and

This paper explores the psychological experiences of domestic workers in three contemporary Latin American films: Roma (Mexico, 2018), Crímenes de familia (Argentina, 2020) and Que Horas Ela Volta? (Brazil, 2015). Specifically, the motherhood of these three protagonists is explored and analyzed using psychological research that pertains to motherhood, trauma, and the relationships between domestic workers and the families that employ them. This paper reveals that contemporary Latin American cinema portrays domestic workers as having negative experiences of motherhood as a direct result of their occupation and proposes for further protections, policy change, and psychological research to take place for domestic workers in Latin America and beyond.

ContributorsSamuels, Alana D (Author) / Tompkins, Cynthia (Thesis director) / Bezerra, Ligia (Committee member) / School of International Letters and Cultures (Contributor) / Department of Psychology (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
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Description

Developed a business product with a team of CS students.

ContributorsPerri, Cole Thomas (Co-author) / Hernandez, Maximilliano (Co-author) / Schneider, Kaitlin (Co-author) / Call, Andy (Thesis director) / Hunt, Neil (Committee member) / School of Accountancy (Contributor) / Watts College of Public Service & Community Solut (Contributor) / WPC Graduate Programs (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
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Curiosity has been linked with many benefits, including increased overall well-being (Lydon-Staley et al., 2020) and greater academic achievement (Gottfried et al., 2016). The value that children place on learning new things and exploring novel ideas is unrivaled by older individuals. However, little research has been conducted to examine

Curiosity has been linked with many benefits, including increased overall well-being (Lydon-Staley et al., 2020) and greater academic achievement (Gottfried et al., 2016). The value that children place on learning new things and exploring novel ideas is unrivaled by older individuals. However, little research has been conducted to examine how parents may be able to help foster their children’s curiosity in a way that teaches them how to effectively search for and synthesize information. This paper aims to determine how parents’ language during a storybook task is related to their children’s strategy to collect rewards during a search game. Preliminary results suggest that parents may be able to encourage more effective search by asking more close-ended questions. These findings provide insight into how parents and guardians may be able to encourage their children to become better adept at searching for information by taking in clues about their environment and modifying their behavior to maximize their efforts.

ContributorsScirpo, Kalie Rose (Author) / Lucca, Kelsey (Thesis director) / Kupfer, Anne (Committee member) / Department of Psychology (Contributor) / Sanford School of Social and Family Dynamics (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
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Description
Bridging semantic gap is one of the fundamental problems in multimedia computing and pattern recognition. The challenge of associating low-level signal with their high-level semantic interpretation is mainly due to the fact that semantics are often conveyed implicitly in a context, relying on interactions among multiple levels of concepts or

Bridging semantic gap is one of the fundamental problems in multimedia computing and pattern recognition. The challenge of associating low-level signal with their high-level semantic interpretation is mainly due to the fact that semantics are often conveyed implicitly in a context, relying on interactions among multiple levels of concepts or low-level data entities. Also, additional domain knowledge may often be indispensable for uncovering the underlying semantics, but in most cases such domain knowledge is not readily available from the acquired media streams. Thus, making use of various types of contextual information and leveraging corresponding domain knowledge are vital for effectively associating high-level semantics with low-level signals with higher accuracies in multimedia computing problems. In this work, novel computational methods are explored and developed for incorporating contextual information/domain knowledge in different forms for multimedia computing and pattern recognition problems. Specifically, a novel Bayesian approach with statistical-sampling-based inference is proposed for incorporating a special type of domain knowledge, spatial prior for the underlying shapes; cross-modality correlations via Kernel Canonical Correlation Analysis is explored and the learnt space is then used for associating multimedia contents in different forms; model contextual information as a graph is leveraged for regulating interactions among high-level semantic concepts (e.g., category labels), low-level input signal (e.g., spatial/temporal structure). Four real-world applications, including visual-to-tactile face conversion, photo tag recommendation, wild web video classification and unconstrained consumer video summarization, are selected to demonstrate the effectiveness of the approaches. These applications range from classic research challenges to emerging tasks in multimedia computing. Results from experiments on large-scale real-world data with comparisons to other state-of-the-art methods and subjective evaluations with end users confirmed that the developed approaches exhibit salient advantages, suggesting that they are promising for leveraging contextual information/domain knowledge for a wide range of multimedia computing and pattern recognition problems.
ContributorsWang, Zhesheng (Author) / Li, Baoxin (Thesis advisor) / Sundaram, Hari (Committee member) / Qian, Gang (Committee member) / Ye, Jieping (Committee member) / Arizona State University (Publisher)
Created2011
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K-Nearest-Neighbors (KNN) search is a fundamental problem in many application domains such as database and data mining, information retrieval, machine learning, pattern recognition and plagiarism detection. Locality sensitive hash (LSH) is so far the most practical approximate KNN search algorithm for high dimensional data. Algorithms such as Multi-Probe LSH and

K-Nearest-Neighbors (KNN) search is a fundamental problem in many application domains such as database and data mining, information retrieval, machine learning, pattern recognition and plagiarism detection. Locality sensitive hash (LSH) is so far the most practical approximate KNN search algorithm for high dimensional data. Algorithms such as Multi-Probe LSH and LSH-Forest improve upon the basic LSH algorithm by varying hash bucket size dynamically at query time, so these two algorithms can answer different KNN queries adaptively. However, these two algorithms need a data access post-processing step after candidates' collection in order to get the final answer to the KNN query. In this thesis, Multi-Probe LSH with data access post-processing (Multi-Probe LSH with DAPP) algorithm and LSH-Forest with data access post-processing (LSH-Forest with DAPP) algorithm are improved by replacing the costly data access post-processing (DAPP) step with a much faster histogram-based post-processing (HBPP). Two HBPP algorithms: LSH-Forest with HBPP and Multi- Probe LSH with HBPP are presented in this thesis, both of them achieve the three goals for KNN search in large scale high dimensional data set: high search quality, high time efficiency, high space efficiency. None of the previous KNN algorithms can achieve all three goals. More specifically, it is shown that HBPP algorithms can always achieve high search quality (as good as LSH-Forest with DAPP and Multi-Probe LSH with DAPP) with much less time cost (one to several orders of magnitude speedup) and same memory usage. It is also shown that with almost same time cost and memory usage, HBPP algorithms can always achieve better search quality than LSH-Forest with random pick (LSH-Forest with RP) and Multi-Probe LSH with random pick (Multi-Probe LSH with RP). Moreover, to achieve a very high search quality, Multi-Probe with HBPP is always a better choice than LSH-Forest with HBPP, regardless of the distribution, size and dimension number of the data set.
ContributorsYu, Renwei (Author) / Candan, Kasim S (Thesis advisor) / Sapino, Maria L (Committee member) / Chen, Yi (Committee member) / Sundaram, Hari (Committee member) / Arizona State University (Publisher)
Created2011
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This thesis deals with the analysis of interpersonal communication dynamics in online social networks and social media. Our central hypothesis is that communication dynamics between individuals manifest themselves via three key aspects: the information that is the content of communication, the social engagement i.e. the sociological framework emergent of the

This thesis deals with the analysis of interpersonal communication dynamics in online social networks and social media. Our central hypothesis is that communication dynamics between individuals manifest themselves via three key aspects: the information that is the content of communication, the social engagement i.e. the sociological framework emergent of the communication process, and the channel i.e. the media via which communication takes place. Communication dynamics have been of interest to researchers from multi-faceted domains over the past several decades. However, today we are faced with several modern capabilities encompassing a host of social media websites. These sites feature variegated interactional affordances, ranging from blogging, micro-blogging, sharing media elements as well as a rich set of social actions such as tagging, voting, commenting and so on. Consequently, these communication tools have begun to redefine the ways in which we exchange information, our modes of social engagement, and mechanisms of how the media characteristics impact our interactional behavior. The outcomes of this research are manifold. We present our contributions in three parts, corresponding to the three key organizing ideas. First, we have observed that user context is key to characterizing communication between a pair of individuals. However interestingly, the probability of future communication seems to be more sensitive to the context compared to the delay, which appears to be rather habitual. Further, we observe that diffusion of social actions in a network can be indicative of future information cascades; that might be attributed to social influence or homophily depending on the nature of the social action. Second, we have observed that different modes of social engagement lead to evolution of groups that have considerable predictive capability in characterizing external-world temporal occurrences, such as stock market dynamics as well as collective political sentiments. Finally, characterization of communication on rich media sites have shown that conversations that are deemed "interesting" appear to have consequential impact on the properties of the social network they are associated with: in terms of degree of participation of the individuals in future conversations, thematic diffusion as well as emergent cohesiveness in activity among the concerned participants in the network. Based on all these outcomes, we believe that this research can make significant contribution into a better understanding of how we communicate online and how it is redefining our collective sociological behavior.
ContributorsDe Choudhury, Munmun (Author) / Sundaram, Hari (Thesis advisor) / Candan, K. Selcuk (Committee member) / Liu, Huan (Committee member) / Watts, Duncan J. (Committee member) / Seligmann, Doree D. (Committee member) / Arizona State University (Publisher)
Created2011