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Description
Graph theory is a critical component of computer science and software engineering, with algorithms concerning graph traversal and comprehension powering much of the largest problems in both industry and research. Engineers and researchers often have an accurate view of their target graph, however they struggle to implement a correct, and efficient, search over that graph.
To facilitate rapid, correct, efficient, and intuitive development of graph based solutions we propose a new programming language construct - the search statement. Given a supra-root node, a procedure which determines the children of a given parent node, and optional definitions of the fail-fast acceptance or rejection of a solution, the search statement can conduct a search over any graph or network. Structurally, this statement is modelled after the common switch statement and is put into a largely imperative/procedural context to allow for immediate and intuitive development by most programmers. The Go programming language has been used as a foundation and proof-of-concept of the search statement. A Go compiler is provided which implements this construct.
To facilitate rapid, correct, efficient, and intuitive development of graph based solutions we propose a new programming language construct - the search statement. Given a supra-root node, a procedure which determines the children of a given parent node, and optional definitions of the fail-fast acceptance or rejection of a solution, the search statement can conduct a search over any graph or network. Structurally, this statement is modelled after the common switch statement and is put into a largely imperative/procedural context to allow for immediate and intuitive development by most programmers. The Go programming language has been used as a foundation and proof-of-concept of the search statement. A Go compiler is provided which implements this construct.
ContributorsHenderson, Christopher (Author) / Bansal, Ajay (Thesis advisor) / Lindquist, Timothy (Committee member) / Acuna, Ruben (Committee member) / Arizona State University (Publisher)
Created2018
Description
UVLabel was created to enable radio astronomers to view and annotate their own data such that they could then expand their future research paths. It simplifies their data rendering process by providing a simple user interface to better access sections of their data. Furthermore, it provides an interface to track trends in their data through a labelling feature.
The tool was developed following the incremental development process in order to quickly create a functional and testable tool. The incremental process also allowed for feedback from radio astronomers to help guide the project's development.
UVLabel provides both a functional product, and a modifiable and scalable code base for radio astronomer developers. This enables astronomers studying various astronomical interferometric data labelling capabilities. The tool can then be used to improve their filtering methods, pursue machine learning solutions, and discover new trends. Finally, UVLabel will be open source to put customization, scalability, and adaptability in the hands of these researchers.
The tool was developed following the incremental development process in order to quickly create a functional and testable tool. The incremental process also allowed for feedback from radio astronomers to help guide the project's development.
UVLabel provides both a functional product, and a modifiable and scalable code base for radio astronomer developers. This enables astronomers studying various astronomical interferometric data labelling capabilities. The tool can then be used to improve their filtering methods, pursue machine learning solutions, and discover new trends. Finally, UVLabel will be open source to put customization, scalability, and adaptability in the hands of these researchers.
ContributorsLa Place, Cecilia (Author) / Bansal, Ajay (Thesis advisor) / Jacobs, Daniel (Thesis advisor) / Acuna, Ruben (Committee member) / Arizona State University (Publisher)
Created2019
Description
Capturing the information in an image into a natural language sentence is
considered a difficult problem to be solved by computers. Image captioning involves not just detecting objects from images but understanding the interactions between the objects to be translated into relevant captions. So, expertise in the fields of computer vision paired with natural language processing are supposed to be crucial for this purpose. The sequence to sequence modelling strategy of deep neural networks is the traditional approach to generate a sequential list of words which are combined to represent the image. But these models suffer from the problem of high variance by not being able to generalize well on the training data.
The main focus of this thesis is to reduce the variance factor which will help in generating better captions. To achieve this, Ensemble Learning techniques have been explored, which have the reputation of solving the high variance problem that occurs in machine learning algorithms. Three different ensemble techniques namely, k-fold ensemble, bootstrap aggregation ensemble and boosting ensemble have been evaluated in this thesis. For each of these techniques, three output combination approaches have been analyzed. Extensive experiments have been conducted on the Flickr8k dataset which has a collection of 8000 images and 5 different captions for every image. The bleu score performance metric, which is considered to be the standard for evaluating natural language processing (NLP) problems, is used to evaluate the predictions. Based on this metric, the analysis shows that ensemble learning performs significantly better and generates more meaningful captions compared to any of the individual models used.
considered a difficult problem to be solved by computers. Image captioning involves not just detecting objects from images but understanding the interactions between the objects to be translated into relevant captions. So, expertise in the fields of computer vision paired with natural language processing are supposed to be crucial for this purpose. The sequence to sequence modelling strategy of deep neural networks is the traditional approach to generate a sequential list of words which are combined to represent the image. But these models suffer from the problem of high variance by not being able to generalize well on the training data.
The main focus of this thesis is to reduce the variance factor which will help in generating better captions. To achieve this, Ensemble Learning techniques have been explored, which have the reputation of solving the high variance problem that occurs in machine learning algorithms. Three different ensemble techniques namely, k-fold ensemble, bootstrap aggregation ensemble and boosting ensemble have been evaluated in this thesis. For each of these techniques, three output combination approaches have been analyzed. Extensive experiments have been conducted on the Flickr8k dataset which has a collection of 8000 images and 5 different captions for every image. The bleu score performance metric, which is considered to be the standard for evaluating natural language processing (NLP) problems, is used to evaluate the predictions. Based on this metric, the analysis shows that ensemble learning performs significantly better and generates more meaningful captions compared to any of the individual models used.
ContributorsKatpally, Harshitha (Author) / Bansal, Ajay (Thesis advisor) / Acuna, Ruben (Committee member) / Gonzalez-Sanchez, Javier (Committee member) / Arizona State University (Publisher)
Created2019
Description
A lot of research can be seen in the field of social robotics that majorly concentrate on various aspects of social robots including design of mechanical parts and their move- ment, cognitive speech and face recognition capabilities. Several robots have been developed with the intention of being social, like humans, without much emphasis on how human-like they actually look, in terms of expressions and behavior. Fur- thermore, a substantial disparity can be seen in the success of results of any research involving ”humanizing” the robots’ behavior, or making it behave more human-like as opposed to research into biped movement, movement of individual body parts like arms, fingers, eyeballs, or human-like appearance itself. The research in this paper in- volves understanding why the research on facial expressions of social humanoid robots fails where it is not accepted completely in the current society owing to the uncanny valley theory. This paper identifies the problem with the current facial expression research as information retrieval problem. This paper identifies the current research method in the design of facial expressions of social robots, followed by using deep learning as similarity evaluation technique to measure the humanness of the facial ex- pressions developed from the current technique and further suggests a novel solution to the facial expression design of humanoids using deep learning.
ContributorsMurthy, Shweta (Author) / Gaffar, Ashraf (Thesis advisor) / Ghazarian, Arbi (Committee member) / Gonzalez-Sanchez, Javier (Committee member) / Arizona State University (Publisher)
Created2017
Description
Today, in a world of automation, the impact of Artificial Intelligence can be seen in every aspect of our lives. Starting from smart homes to self-driving cars everything is run using intelligent, adaptive technologies. In this thesis, an attempt is made to analyze the correlation between driving quality and its impact on the use of car infotainment system and vice versa and hence the driver distraction. Various internal and external driving factors have been identified to understand the dependency and seriousness of driver distraction caused due to the car infotainment system. We have seen a number UI/UX changes, speech recognition advancements in cars to reduce distraction. But reducing the number of casualties on road is still a persisting problem in hand as the cognitive load on the driver is considered to be one of the primary reasons for distractions leading to casualties. In this research, a pathway has been provided to move towards building an artificially intelligent, adaptive and interactive infotainment which is trained to behave differently by analyzing the driving quality without the intervention of the driver. The aim is to not only shift focus of the driver from screen to street view, but to also change the inherent behavior of the infotainment system based on the driving statistics at that point in time without the need for driver intervention.
ContributorsSuresh, Seema (Author) / Gaffar, Ashraf (Thesis advisor) / Sodemann, Angela (Committee member) / Gonzalez-Sanchez, Javier (Committee member) / Arizona State University (Publisher)
Created2017
Description
Subdivision surfaces have gained more and more traction since it became the standard surface representation in the movie industry for many years. And Catmull-Clark subdivision scheme is the most popular one for handling polygonal meshes. After its introduction, Catmull-Clark surfaces have been extended to several eminent ways, including the handling of boundaries, infinitely sharp creases, semi-sharp creases, and hierarchically defined detail. For ray tracing of subdivision surfaces, a common way is to construct spatial bounding volume hierarchies on top of input control mesh. However, a high-level refined subdivision surface not only requires a substantial amount of memory storage, but also causes slow and inefficient ray tracing. In this thesis, it presents a new way to improve the efficiency of ray tracing of subdivision surfaces, while the quality is not as good as general methods.
ContributorsKe, Shujian (Author) / Amresh, Ashish (Thesis advisor) / Femiani, John (Committee member) / Gonzalez-Sanchez, Javier (Committee member) / Arizona State University (Publisher)
Created2017
Description
Affect signals what humans care about and is involved in rational decision-making and action selection. Many technologies may be improved by the capability to recognize human affect and to respond adaptively by appropriately modifying their operation. This capability, named affect-driven self-adaptation, benefits systems as diverse as learning environments, healthcare applications, and video games, and indeed has the potential to improve systems that interact intimately with users across all sectors of society. The main challenge is that existing approaches to advancing affect-driven self-adaptive systems typically limit their applicability by supporting the creation of one-of-a-kind systems with hard-wired affect recognition and self-adaptation capabilities, which are brittle, costly to change, and difficult to reuse. A solution to this limitation is to leverage the development of affect-driven self-adaptive systems with a manufacturing vision.
This dissertation demonstrates how using a software product line paradigm can jumpstart the development of affect-driven self-adaptive systems with that manufacturing vision. Applying a software product line approach to the affect-driven self-adaptive domain provides a comprehensive, flexible and reusable infrastructure of components with mechanisms to monitor a user’s affect and his/her contextual interaction with a system, to detect opportunities for improvements, to select a course of action, and to effect changes. It also provides a domain-specific architecture and well-documented process guidelines, which facilitate an understanding of the organization of affect-driven self-adaptive systems and their implementation by systematically customizing the infrastructure to effectively address the particular requirements of specific systems.
The software product line approach is evaluated by applying it in the development of learning environments and video games that demonstrate the significant potential of the solution, across diverse development scenarios and applications.
The key contributions of this work include extending self-adaptive system modeling, implementing a reusable infrastructure, and leveraging the use of patterns to exploit the commonalities between systems in the affect-driven self-adaptation domain.
This dissertation demonstrates how using a software product line paradigm can jumpstart the development of affect-driven self-adaptive systems with that manufacturing vision. Applying a software product line approach to the affect-driven self-adaptive domain provides a comprehensive, flexible and reusable infrastructure of components with mechanisms to monitor a user’s affect and his/her contextual interaction with a system, to detect opportunities for improvements, to select a course of action, and to effect changes. It also provides a domain-specific architecture and well-documented process guidelines, which facilitate an understanding of the organization of affect-driven self-adaptive systems and their implementation by systematically customizing the infrastructure to effectively address the particular requirements of specific systems.
The software product line approach is evaluated by applying it in the development of learning environments and video games that demonstrate the significant potential of the solution, across diverse development scenarios and applications.
The key contributions of this work include extending self-adaptive system modeling, implementing a reusable infrastructure, and leveraging the use of patterns to exploit the commonalities between systems in the affect-driven self-adaptation domain.
ContributorsGonzalez-Sanchez, Javier (Author) / Burleson, Winslow (Thesis advisor) / Collofello, James (Thesis advisor) / Garlan, David (Committee member) / Sarjoughian, Hessam S. (Committee member) / Atkinson, Robert (Committee member) / Arizona State University (Publisher)
Created2016
Description
Feedback represents a vital component of the learning process and is especially important for Computer Science students. With class sizes that are often large, it can be challenging to provide individualized feedback to students. Consistent, constructive, supportive feedback through a tutoring companion can scaffold the learning process for students.
This work contributes to the construction of a tutoring companion designed to provide this feedback to students. It aims to bridge the gap between the messages the compiler delivers, and the support required for a novice student to understand the problem and fix their code. Particularly, it provides support for students learning about recursion in a beginning university Java programming course. Besides also providing affective support, a tutoring companion could be more effective when it is embedded into the environment that the student is already using, instead of an additional tool for the student to learn. The proposed Tutoring Companion is embedded into the Eclipse Integrated Development Environment (IDE).
This thesis focuses on the reasoning model for the Tutoring Companion and is developed using the techniques of a neural network. While a student uses the IDE, the Tutoring Companion collects 16 data points, including the presence of certain key words, cyclomatic complexity, and error messages from the compiler, every time it detects an event, such as a run attempt, debug attempt, or a request for help, in the IDE. This data is used as inputs to the neural network. The neural network produces a correlating single output code for the feedback to be provided to the student, which is displayed in the IDE.
The effectiveness of the approach is examined among 38 Computer Science students who solve a programming assignment while the Tutoring Companion assists them. Data is collected from these interactions, including all inputs and outputs for the neural network, and students are surveyed regarding their experience. Results suggest that students feel supported while working with the Companion and promising potential for using a neural network with an embedded companion in the future. Challenges in developing an embedded companion are discussed, as well as opportunities for future work.
This work contributes to the construction of a tutoring companion designed to provide this feedback to students. It aims to bridge the gap between the messages the compiler delivers, and the support required for a novice student to understand the problem and fix their code. Particularly, it provides support for students learning about recursion in a beginning university Java programming course. Besides also providing affective support, a tutoring companion could be more effective when it is embedded into the environment that the student is already using, instead of an additional tool for the student to learn. The proposed Tutoring Companion is embedded into the Eclipse Integrated Development Environment (IDE).
This thesis focuses on the reasoning model for the Tutoring Companion and is developed using the techniques of a neural network. While a student uses the IDE, the Tutoring Companion collects 16 data points, including the presence of certain key words, cyclomatic complexity, and error messages from the compiler, every time it detects an event, such as a run attempt, debug attempt, or a request for help, in the IDE. This data is used as inputs to the neural network. The neural network produces a correlating single output code for the feedback to be provided to the student, which is displayed in the IDE.
The effectiveness of the approach is examined among 38 Computer Science students who solve a programming assignment while the Tutoring Companion assists them. Data is collected from these interactions, including all inputs and outputs for the neural network, and students are surveyed regarding their experience. Results suggest that students feel supported while working with the Companion and promising potential for using a neural network with an embedded companion in the future. Challenges in developing an embedded companion are discussed, as well as opportunities for future work.
ContributorsDay, Melissa (Author) / Gonzalez-Sanchez, Javier (Thesis advisor) / Bansal, Ajay (Committee member) / Mehlhase, Alexandra (Committee member) / Arizona State University (Publisher)
Created2019
Description
SLAM (Simultaneous Localization and Mapping) is a problem that has existed for a long time in robotics and autonomous navigation. The objective of SLAM is for a robot to simultaneously figure out its position in space and map its environment. SLAM is especially useful and mandatory for robots that want to navigate autonomously. The description might make it seem like a chicken and egg problem, but numerous methods have been proposed to tackle SLAM. Before the rise in the popularity of deep learning and AI (Artificial Intelligence), most existing algorithms involved traditional hard-coded algorithms that would receive and process sensor information and convert it into some solvable sensor-agnostic problem. The challenge for these sorts of methods is having to tackle dynamic environments. The more variety in the environment, the poorer the results. Also due to the increase in computational power and the capability of deep learning-based image processing, visual SLAM has become extremely viable and maybe even preferable to traditional SLAM algorithms. In this research, a deep learning-based solution to the SLAM problem is proposed, specifically monocular visual SLAM which is solving the problem of SLAM purely with a singular camera as the input, and the model is tested on the KITTI (Karlsruhe Institute of Technology & Toyota Technological Institute) odometry dataset.
ContributorsRupaakula, Krishna Sandeep (Author) / Bansal, Ajay (Thesis advisor) / Baron, Tyler (Committee member) / Acuna, Ruben (Committee member) / Arizona State University (Publisher)
Created2023
Description
Plagiarism is a huge problem in a learning environment. In programming classes especially, plagiarism can be hard to detect as source codes' appearance can be easily modified without changing the intent through simple formatting changes or refactoring. There are a number of plagiarism detection tools that attempt to encode knowledge about the programming languages they support in order to better detect obscured duplicates. Many such tools do not support a large number of languages because doing so requires too much code and therefore too much maintenance. It is also difficult to add support for new languages because each language is vastly different syntactically. Tools that are more extensible often do so by reducing the features of a language that are encoded and end up closer to text comparison tools than structurally-aware program analysis tools.
Kitsune attempts to remedy these issues by tying itself to Antlr, a pre-existing language recognition tool with over 200 currently supported languages. In addition, it provides an interface through which generic manipulations can be applied to the parse tree generated by Antlr. As Kitsune relies on language-agnostic structure modifications, it can be adapted with minimal effort to provide plagiarism detection for new languages. Kitsune has been evaluated for 10 of the languages in the Antlr grammar repository with success and could easily be extended to support all of the grammars currently developed by Antlr or future grammars which are developed as new languages are written.
Kitsune attempts to remedy these issues by tying itself to Antlr, a pre-existing language recognition tool with over 200 currently supported languages. In addition, it provides an interface through which generic manipulations can be applied to the parse tree generated by Antlr. As Kitsune relies on language-agnostic structure modifications, it can be adapted with minimal effort to provide plagiarism detection for new languages. Kitsune has been evaluated for 10 of the languages in the Antlr grammar repository with success and could easily be extended to support all of the grammars currently developed by Antlr or future grammars which are developed as new languages are written.
ContributorsMonroe, Zachary Lynn (Author) / Bansal, Ajay (Thesis advisor) / Lindquist, Timothy (Committee member) / Acuna, Ruben (Committee member) / Arizona State University (Publisher)
Created2020