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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
In this project, the use of deep neural networks for the process of selecting actions to execute within an environment to achieve a goal is explored. Scenarios like this are common in crafting based games such as Terraria or Minecraft. Goals in these environments have recursive sub-goal dependencies which form a dependency tree. An agent operating within these environments have access to low amounts of data about the environment before interacting with it, so it is crucial that this agent is able to effectively utilize a tree of dependencies and its environmental surroundings to make judgements about which sub-goals are most efficient to pursue at any point in time. A successful agent aims to minimizes cost when completing a given goal. A deep neural network in combination with Q-learning techniques was employed to act as the agent in this environment. This agent consistently performed better than agents using alternate models (models that used dependency tree heuristics or human-like approaches to make sub-goal oriented choices), with an average performance advantage of 33.86% (with a standard deviation of 14.69%) over the best alternate agent. This shows that machine learning techniques can be consistently employed to make goal-oriented choices within an environment with recursive sub-goal dependencies and low amounts of pre-known information.
ContributorsKoleber, Derek (Author) / Acuna, Ruben (Thesis director) / Bansal, Ajay (Committee member) / W.P. Carey School of Business (Contributor) / Software Engineering (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
The functional programming paradigm is able to provide clean and concise solutions to many common programming problems, as well as promote safer, more testable code by encouraging an isolation of state-modifying behavior. Functional programming is finding its way into traditionally object-oriented and imperative languages, most notably with the introduction of Java 8 and in LINQ for C#. However, no functional programming language has achieved widespread adoption, meaning that students without a formal computer science background who learn technology on-demand for personal projects or for business may not come across functional programming in a significant way. Programmers need a reason to spend time learning these concepts to not miss out on the subtle but profound benefits they provide. I propose the use of a video game as an environment in which learning functional programming is the player's goal. In this carefully constructed video game, learning functional programming is the key to progression. Players will be motivated to learn and will be given an immediate chance to test and demonstrate their understanding. The game, named Lambda Starship (stylized as (lambda () starship)), is a 3D first-person video game. It takes place in a spaceship that, due to extreme magnetic interference, has lost all on-board software while leaving the hardware completely intact. The player is tasked to write software using functional programming paradigms to replace the old software and bring the spaceship back to a working state. Throughout the process, the player is guided by an in-game manual and other descriptive resources. The game is implemented in Unity and scripted using C#. The game's educational and entertainment value was evaluated with a study case. 24 undergraduate students at Arizona State University (ASU) played the game and were surveyed detailing their experience. During play, user statistics were recorded automatically, providing a data-driven way to analyze where players struggled with the concepts introduced in the game. Reception was neutral or positive in both the entertainment and educational sides of the game. A few players expressed concerns about the manual in its form factor and engagement value.
ContributorsCompton, Tyler Alexander (Author) / Gonzalez-Sanchez, Javier (Thesis director) / Bansal, Srividya (Committee member) / Software Engineering (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Brains and computers have been interacting since the invention of the computer. These two entities have worked together to accomplish a monumental set of goals, from landing man on the moon to helping to understand how the universe works on the most microscopic levels, and everything in between. As the years have gone on, the extent and depth of interaction between brains and computers have consistently widened, to the point where computers help brains with their thinking in virtually infinite everyday situations around the world. The first purpose of this research project was to conduct a brief review for the purposes of gaining a sound understanding of how both brains and computers operate at fundamental levels, and what it is about these two entities that allow them to work evermore seamlessly as the years go on. Next, a history of interaction between brains and computers was developed, which expanded upon the first task and helped to contribute to visions of future brain-computer interaction (BCI). The subsequent and primary task of this research project was to develop a theoretical framework for a potential brain-aiding device of the future. This was done by conducting an extensive literature review regarding the most advanced BCI technology in modern times and expanding upon the findings to argue feasibility of the future device and its components. Next, social predictions regarding the acceptance and use of the new technology were made by designing and executing a survey based on the Unified Theory of the Acceptance and Use of Technology (UTAUT). Finally, general economic predictions were inferred by examining several relationships between money and computers over time.
ContributorsThum, Giuseppe Edwardo (Author) / Gaffar, Ashraf (Thesis director) / Gonzalez-Sanchez, Javier (Committee member) / College of Integrative Sciences and Arts (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
Description
The Internet of Things (IoT) is term used to refer to the billions of Internet connected, embedded devices that communicate with one another with the purpose of sharing data or performing actions. One of the core usages of the proverbial network is the ability for its devices and services to interact with one another to automate daily tasks and routines. For example, IoT devices are often used to automate tasks within the household, such as turning the lights on/off or starting the coffee pot. However, designing a modular system to create and schedule these routines is a difficult task.
Current IoT integration utilities attempt to help simplify this task, but most fail to satisfy one of the requirements many users want in such a system ‒ simplified integration with third party devices. This project seeks to solve this issue through the creation of an easily extendable, modular integrating utility. It is open-source and does not require the use of a cloud-based server, with users hosting the server themselves. With a server and data controller implemented in pure Python and a library for embedded ESP8266 microcontroller-powered devices, the solution seeks to satisfy both casual users as well as those interested in developing their own integrations.
Current IoT integration utilities attempt to help simplify this task, but most fail to satisfy one of the requirements many users want in such a system ‒ simplified integration with third party devices. This project seeks to solve this issue through the creation of an easily extendable, modular integrating utility. It is open-source and does not require the use of a cloud-based server, with users hosting the server themselves. With a server and data controller implemented in pure Python and a library for embedded ESP8266 microcontroller-powered devices, the solution seeks to satisfy both casual users as well as those interested in developing their own integrations.
ContributorsBeagle, Bryce Edward (Author) / Acuna, Ruben (Thesis director) / Jordan, Shawn (Committee member) / Engineering Programs (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
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
In today's data-driven world, every datum is connected to a large amount of data. Relational databases have been proving itself a pioneer in the field of data storage and manipulation since 1970s. But more recently they have been challenged by NoSQL graph databases in handling data models which have an inherent graphical representation. Graph databases with the ability to store physical relationships between two nodes and native graph processing technique have been doing exceptionally well in graph data storage and management for applications like recommendation engines, biological modeling, network modeling, social media applications, etc.
Instructional Module Development System (IMODS) is a web-based software system that guides STEM instructors through the complex task of curriculum design, ensures tight alignment between various components of a course (i.e., learning objectives, content, assessments), and provides relevant information about research-based pedagogical and assessment strategies. The data model of IMODS is highly connected and has an inherent graphical representation between all its entities with numerous relationships between them. This thesis focuses on developing an algorithm to determine completeness of course design developed using IMODS. As part of this research objective, the study also analyzes the data model for best fit database to run these algorithms. As part of this thesis, two separate applications abstracting the data model of IMODS have been developed - one with Neo4j (graph database) and another with PostgreSQL (relational database). The research objectives of the thesis are as follows: (i) evaluate the performance of Neo4j and PostgreSQL in handling complex queries that will be fired throughout the life cycle of the course design process; (ii) devise an algorithm to determine the completeness of a course design developed using IMODS. This thesis presents the process of creating data model for PostgreSQL and converting it into a graph data model to be abstracted by Neo4j, creating SQL and CYPHER scripts for undertaking experiments on both platforms, testing and elaborate analysis of the results and evaluation of the databases in the context of IMODS.
Instructional Module Development System (IMODS) is a web-based software system that guides STEM instructors through the complex task of curriculum design, ensures tight alignment between various components of a course (i.e., learning objectives, content, assessments), and provides relevant information about research-based pedagogical and assessment strategies. The data model of IMODS is highly connected and has an inherent graphical representation between all its entities with numerous relationships between them. This thesis focuses on developing an algorithm to determine completeness of course design developed using IMODS. As part of this research objective, the study also analyzes the data model for best fit database to run these algorithms. As part of this thesis, two separate applications abstracting the data model of IMODS have been developed - one with Neo4j (graph database) and another with PostgreSQL (relational database). The research objectives of the thesis are as follows: (i) evaluate the performance of Neo4j and PostgreSQL in handling complex queries that will be fired throughout the life cycle of the course design process; (ii) devise an algorithm to determine the completeness of a course design developed using IMODS. This thesis presents the process of creating data model for PostgreSQL and converting it into a graph data model to be abstracted by Neo4j, creating SQL and CYPHER scripts for undertaking experiments on both platforms, testing and elaborate analysis of the results and evaluation of the databases in the context of IMODS.
ContributorsSaha, Abir Lal (Author) / Bansal, Srividya (Thesis advisor) / Bansal, Ajay (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