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Description
Artificial Intelligence’s facial recognition programs are inherently racially biased. The programs are not necessarily created with the intent to disproportionately impact marginalized communities, but through their data mining process of learning, they can become biased as the data they use may train them to think in a biased manner. Biased data is difficult to spot as the programming field is homogeneous and this issue reflects underlying societal biases. Facial recognition programs do not identify minorities at the same rate as their Caucasian counterparts leading to false positives in identifications and an increase of run-ins with the law. AI does not have the ability to role-reverse judge as a human does and therefore its use should be limited until a more equitable program is developed and thoroughly tested.
ContributorsGurtler, Charles William (Author) / Iheduru, Okechukwu (Thesis director) / Fette, Donald (Committee member) / Economics Program in CLAS (Contributor) / School of Politics and Global Studies (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
This paper is centered on the use of generative adversarial networks (GANs) to convert or generate RGB images from grayscale ones. The primary goal is to create sensible and colorful versions of a set of grayscale images by training a discriminator to recognize failed or generated images and training a generator to attempt to satisfy the discriminator. The network design is described in further detail below; however there are several potential issues that arise including the averaging of a color for certain images such that small details in an image are not assigned unique colors leading to a neutral blend. We attempt to mitigate this issue as much as possible.
ContributorsLobo, Ian (Co-author) / Koleber, Keith (Co-author) / Markabawi, Jah (Co-author) / Masud, Abdullah (Co-author) / Yang, Yingzhen (Thesis director) / Wang, Yancheng (Committee member) / Computer Science and Engineering Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
This dissertation transforms a set of system complexity reduction problems to feature selection problems. Three systems are considered: classification based on association rules, network structure learning, and time series classification. Furthermore, two variable importance measures are proposed to reduce the feature selection bias in tree models. Associative classifiers can achieve high accuracy, but the combination of many rules is difficult to interpret. Rule condition subset selection (RCSS) methods for associative classification are considered. RCSS aims to prune the rule conditions into a subset via feature selection. The subset then can be summarized into rule-based classifiers. Experiments show that classifiers after RCSS can substantially improve the classification interpretability without loss of accuracy. An ensemble feature selection method is proposed to learn Markov blankets for either discrete or continuous networks (without linear, Gaussian assumptions). The method is compared to a Bayesian local structure learning algorithm and to alternative feature selection methods in the causal structure learning problem. Feature selection is also used to enhance the interpretability of time series classification. Existing time series classification algorithms (such as nearest-neighbor with dynamic time warping measures) are accurate but difficult to interpret. This research leverages the time-ordering of the data to extract features, and generates an effective and efficient classifier referred to as a time series forest (TSF). The computational complexity of TSF is only linear in the length of time series, and interpretable features can be extracted. These features can be further reduced, and summarized for even better interpretability. Lastly, two variable importance measures are proposed to reduce the feature selection bias in tree-based ensemble models. It is well known that bias can occur when predictor attributes have different numbers of values. Two methods are proposed to solve the bias problem. One uses an out-of-bag sampling method called OOBForest, and the other, based on the new concept of a partial permutation test, is called a pForest. Experimental results show the existing methods are not always reliable for multi-valued predictors, while the proposed methods have advantages.
ContributorsDeng, Houtao (Author) / Runger, George C. (Thesis advisor) / Lohr, Sharon L (Committee member) / Pan, Rong (Committee member) / Zhang, Muhong (Committee member) / Arizona State University (Publisher)
Created2011
Description
Real-world environments are characterized by non-stationary and continuously evolving data. Learning a classification model on this data would require a framework that is able to adapt itself to newer circumstances. Under such circumstances, transfer learning has come to be a dependable methodology for improving classification performance with reduced training costs and without the need for explicit relearning from scratch. In this thesis, a novel instance transfer technique that adapts a "Cost-sensitive" variation of AdaBoost is presented. The method capitalizes on the theoretical and functional properties of AdaBoost to selectively reuse outdated training instances obtained from a "source" domain to effectively classify unseen instances occurring in a different, but related "target" domain. The algorithm is evaluated on real-world classification problems namely accelerometer based 3D gesture recognition, smart home activity recognition and text categorization. The performance on these datasets is analyzed and evaluated against popular boosting-based instance transfer techniques. In addition, supporting empirical studies, that investigate some of the less explored bottlenecks of boosting based instance transfer methods, are presented, to understand the suitability and effectiveness of this form of knowledge transfer.
ContributorsVenkatesan, Ashok (Author) / Panchanathan, Sethuraman (Thesis advisor) / Li, Baoxin (Committee member) / Ye, Jieping (Committee member) / Arizona State University (Publisher)
Created2011
Description
This study examines the experiences of participants enrolled in an online community college jazz history course. I surveyed the participants before the course began and observed them in the online space through the duration of the course. Six students also participated in interviews during and after the course. Coded data from the interviews, surveys, and recorded discussion posts and journal entries provided evidence about the nature of interaction and engagement in learning in an online environment. I looked for evidence either supporting or detracting from a democratic online learning environment, concentrating on the categories of student engagement, freedom of expression, and accessibility. The data suggested that the participants' behaviors in and abilities to navigate the online class were influenced by their pre-existing native media habits. Participants' reasons for enrolling in the online course, which included convenience and schedule flexibility, informed their actions and behaviors in the class. Analysis revealed that perceived positive student engagement did not contribute to a democratic learning environment but rather to an easy, convenient experience in the online class. Finally, the data indicated that participants' behaviors in their future lives would not be affected by the online class in that their learning experiences were not potent enough to alter or inform their behavior in society. As online classes gain popularity, the ability of these classes to provide meaningful learning experiences must be questioned. Students in this online jazz history class presented, at times, a façade of participation and community building but demonstrated a lack of sincerity and interest in the course. The learning environment supported accessibility and freedom of expression to an extent, but students' engagement with their peers was limited. Overall, this study found a need for more research into the quality of online classes as learning platforms that support democracy, student-to-student interaction, and community building.
ContributorsHunter, Robert W. (Author) / Stauffer, Sandra L (Thesis advisor) / Tobias, Evan (Thesis advisor) / Bush, Jeffrey (Committee member) / Kocour, Michael (Committee member) / Pilafian, Sam (Committee member) / Arizona State University (Publisher)
Created2011
Description
TaxiWorld is a Matlab simulation of a city with a fleet of taxis which operate within it, with the goal of transporting passengers to their destinations. The size of the city, as well as the number of available taxis and the frequency and general locations of fare appearances can all be set on a scenario-by-scenario basis. The taxis must attempt to service the fares as quickly as possible, by picking each one up and carrying it to its drop-off location. The TaxiWorld scenario is formally modeled using both Decentralized Partially-Observable Markov Decision Processes (Dec-POMDPs) and Multi-agent Markov Decision Processes (MMDPs). The purpose of developing formal models is to learn how to build and use formal Markov models, such as can be given to planners to solve for optimal policies in problem domains. However, finding optimal solutions for Dec-POMDPs is NEXP-Complete, so an empirical algorithm was also developed as an improvement to the method already in use on the simulator, and the methods were compared in identical scenarios to determine which is more effective. The empirical method is of course not optimal - rather, it attempts to simply account for some of the most important factors to achieve an acceptable level of effectiveness while still retaining a reasonable level of computational complexity for online solving.
ContributorsWhite, Christopher (Author) / Kambhampati, Subbarao (Thesis advisor) / Gupta, Sandeep (Committee member) / Varsamopoulos, Georgios (Committee member) / Arizona State University (Publisher)
Created2011
Description
Action language C+ is a formalism for describing properties of actions, which is based on nonmonotonic causal logic. The definite fragment of C+ is implemented in the Causal Calculator (CCalc), which is based on the reduction of nonmonotonic causal logic to propositional logic. This thesis describes the language of CCalc in terms of answer set programming (ASP), based on the translation of nonmonotonic causal logic to formulas under the stable model semantics. I designed a standard library which describes the constructs of the input language of CCalc in terms of ASP, allowing a simple modular method to represent CCalc input programs in the language of ASP. Using the combination of system F2LP and answer set solvers, this method achieves functionality close to that of CCalc while taking advantage of answer set solvers to yield efficient computation that is orders of magnitude faster than CCalc for many benchmark examples. In support of this, I created an automated translation system Cplus2ASP that implements the translation and encoding method and automatically invokes the necessary software to solve the translated input programs.
ContributorsCasolary, Michael (Author) / Lee, Joohyung (Thesis advisor) / Ahn, Gail-Joon (Committee member) / Baral, Chitta (Committee member) / Arizona State University (Publisher)
Created2011
Description
Concerto for Piano and Chamber Orchestra was conceived in February of 2013, and conceptually it is my attempt to fuse personal expressions of jazz and classical music into one fully realized statement. It is a three movement work (fast, slow, fast) for 2 fl., 2 ob., 2 cl., bsn., 2 hrn., 2 tpt., tbn., pno., perc., str. (6,4,2,2,1). The work is approximately 27 minutes in duration. The first movement of the Concerto is written in a fluid sonata form. A fugato begins where the second theme would normally appear, and the second theme does not fully appear until near the end of the solo piano section. The result is that the second theme when finally revealed is so reminiscent of the history of jazz and classical synthesis that it does not sound completely new, and in fact is a return of something that was heard before, but only hinted at in this piece. The second movement is a kind of deconstructive set of variations, with a specific theme and harmonic pattern implied throughout the movement. However, the full theme is not disclosed until the final variation. The variations are interrupted by moments of pure rhythmic music, containing harmony made up of major chords with an added fourth, defying resolution, and dissolving each time back into a new variation. The third movement is in rondo form, using rhythmic and harmonic influences from jazz. The percussion plays a substantial role in this movement, acting as a counterpoint to the piano part throughout. This movement and the piece concludes with an extended coda, inspired indirectly by the simple complexities of an improvisational piano solo, building in complexity as the concerto draws to a close.
ContributorsSneider, Elliot (Author) / Rogers, Rodney (Thesis advisor) / DeMars, James (Committee member) / Hackbarth, Glenn (Committee member) / Solis, Theodore (Committee member) / Arizona State University (Publisher)
Created2013
Description
The Vocal Jazz ensemble, a uniquely American choral form, has grown and flourished in the past half century largely through the efforts of professionals and educators throughout the collegiate music community. This document provides historical data as presented through live and published interviews with key individuals involved in the early development of collegiate Vocal Jazz, as well as those who continue this effort currently. It also offers a study of the most influential creative forces that provided the spark for everyone else's fire. A frank discussion on the obstacles encountered and overcome is central to the overall theme of this research into a genre that has moved from a marginalized afterthought to a legitimate, more widely accepted art form. In addition to the perspective provided to future generations of educators in this field, this document also discusses the role of collegiate music academia in preserving and promoting the Vocal Jazz ensemble. The discussion relies on recent data showing the benefits of Vocal Jazz training and the need for authenticity towards its universal integration into college and university vocal performance and music education training.
ContributorsAmerind, Gregory (Author) / Kocour, Michael (Thesis advisor) / Carpenter, Ellon (Committee member) / Britton, David (Committee member) / Ryan, Russell (Committee member) / Arizona State University (Publisher)
Created2013
Description
Linear Temporal Logic is gaining increasing popularity as a high level specification language for robot motion planning due to its expressive power and scalability of LTL control synthesis algorithms. This formalism, however, requires expert knowledge and makes it inaccessible to non-expert users. This thesis introduces a graphical specification environment to create high level motion plans to control robots in the field by converting a visual representation of the motion/task plan into a Linear Temporal Logic (LTL) specification. The visual interface is built on the Android tablet platform and provides functionality to create task plans through a set of well defined gestures and on screen controls. It uses the notion of waypoints to quickly and efficiently describe the motion plan and enables a variety of complex Linear Temporal Logic specifications to be described succinctly and intuitively by the user without the need for the knowledge and understanding of LTL specification. Thus, it opens avenues for its use by personnel in military, warehouse management, and search and rescue missions. This thesis describes the construction of LTL for various scenarios used for robot navigation using the visual interface developed and leverages the use of existing LTL based motion planners to carry out the task plan by a robot.
ContributorsSrinivas, Shashank (Author) / Fainekos, Georgios (Thesis advisor) / Baral, Chitta (Committee member) / Burleson, Winslow (Committee member) / Arizona State University (Publisher)
Created2013