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This thesis describes a synthetic task environment, CyberCog, created for the purposes of 1) understanding and measuring individual and team situation awareness in the context of a cyber security defense task and 2) providing a context for evaluating algorithms, visualizations, and other interventions that are intended to improve cyber situation awareness. CyberCog provides an interactive environment for conducting human-in-loop experiments in which the participants of the experiment perform the tasks of a cyber security defense analyst in response to a cyber-attack scenario. CyberCog generates the necessary performance measures and interaction logs needed for measuring individual and team cyber situation awareness. Moreover, the CyberCog environment provides good experimental control for conducting effective situation awareness studies while retaining realism in the scenario and in the tasks performed.
ContributorsRajivan, Prashanth (Author) / Femiani, John (Thesis advisor) / Cooke, Nancy J. (Thesis advisor) / Lindquist, Timothy (Committee member) / Gary, Kevin (Committee member) / Arizona State University (Publisher)
Created2011
Description
Affective computing allows computers to monitor and influence people’s affects, in other words emotions. Currently, there is a lot of research exploring what can be done with this technology. There are many fields, such as education, healthcare, and marketing, that this technology can transform. However, it is important to question what should be done. There are unique ethical considerations in regards to affective computing that haven't been explored. The purpose of this study is to understand the user’s perspective of affective computing in regards to the Association of Computing Machinery (ACM) Code of Ethics, to ultimately start developing a better understanding of these ethical concerns. For this study, participants were required to watch three different videos and answer a questionnaire, all while wearing an Emotiv EPOC+ EEG headset that measures their emotions. Using the information gathered, the study explores the ethics of affective computing through the user’s perspective.
ContributorsInjejikian, Angelica (Author) / Gonzalez-Sanchez, Javier (Thesis director) / Chavez-Echeagaray, Maria Elena (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
Smart home system (SHS) is a kind of information system aiming at realizing home automation. The SHS can connect with almost any kind of electronic/electric device used in a home so that they can be controlled and monitored centrally. Today's technology also allows the home owners to control and monitor the SHS installed in their homes remotely. This is typically realized by giving the SHS network access ability. Although the SHS's network access ability brings a lot of conveniences to the home owners, it also makes the SHS facing more security threats than ever before. As a result, when designing a SHS, the security threats it might face should be given careful considerations. System security threats can be solved properly by understanding them and knowing the parts in the system that should be protected against them first. This leads to the idea of solving the security threats a SHS might face from the requirements engineering level. Following this idea, this paper proposes a systematic approach to generate the security requirements specifications for the SHS. It can be viewed as the first step toward the complete SHS security requirements engineering process.
ContributorsXu, Rongcao (Author) / Ghazarian, Arbi (Thesis advisor) / Bansal, Ajay (Committee member) / Lindquist, Timothy (Committee member) / Arizona State University (Publisher)
Created2013
Description
The pay-as-you-go economic model of cloud computing increases the visibility, traceability, and verifiability of software costs. Application developers must understand how their software uses resources when running in the cloud in order to stay within budgeted costs and/or produce expected profits. Cloud computing's unique economic model also leads naturally to an earn-as-you-go profit model for many cloud based applications. These applications can benefit from low level analyses for cost optimization and verification. Testing cloud applications to ensure they meet monetary cost objectives has not been well explored in the current literature. When considering revenues and costs for cloud applications, the resource economic model can be scaled down to the transaction level in order to associate source code with costs incurred while running in the cloud. Both static and dynamic analysis techniques can be developed and applied to understand how and where cloud applications incur costs. Such analyses can help optimize (i.e. minimize) costs and verify that they stay within expected tolerances. An adaptation of Worst Case Execution Time (WCET) analysis is presented here to statically determine worst case monetary costs of cloud applications. This analysis is used to produce an algorithm for determining control flow paths within an application that can exceed a given cost threshold. The corresponding results are used to identify path sections that contribute most to cost excess. A hybrid approach for determining cost excesses is also presented that is comprised mostly of dynamic measurements but that also incorporates calculations that are based on the static analysis approach. This approach uses operational profiles to increase the precision and usefulness of the calculations.
ContributorsBuell, Kevin, Ph.D (Author) / Collofello, James (Thesis advisor) / Davulcu, Hasan (Committee member) / Lindquist, Timothy (Committee member) / Sen, Arunabha (Committee member) / Arizona State University (Publisher)
Created2012
Description
In this paper, I explore practical applications of neural networks for automated skin lesion identification. The visual characteristics are of primary importance in the recognition of skin diseases, hence, the development of deep neural network models proven capable of classifying skin lesions can potentially change the face of modern medicine by extending the availability and lowering the cost of diagnostic care. Previous work has demonstrated the effectiveness of convolutional neural networks in image classification in general, with even higher accuracy achievable by data augmentation techniques, such as cropping, rotating, and flipping input images, along with more advanced computationally intensive approaches. In this research, I provide an overview of Convolutional Neural Networks (CNN) and CNN implementation with TensorFlow and Keras API in context of image recognition and classification. I also experiment with custom convolutional neural network model architecture trained using HAM10000 dataset. The dataset used for the case study is obtained from Harvard Dataverse and is maintained by Medical University of Vienna. The HAM10000 dataset is a large collection of multi-source dermatoscopic images of common pigmented skin lesions and is available for academic research under Creative Commons Attribution-Noncommercial 4.0 International Public License. With over ten thousand dermatoscopic images of seven classes of benign and malignant skin lesions, the dataset is substantial for academic machine learning purposes for multiclass image classification. I discuss the successes and shortcomings of the model in respect to its application to the dataset.
ContributorsKaraliova, Natallia (Author) / Bansal, Ajay (Thesis director) / Gonzalez-Sanchez, Javier (Committee member) / Software Engineering (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
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
This mobile application development sought to accomplish three goals: learning mobile development, addressing a real world problem, and applying four years of schooling towards a structured project. These goals were each chosen for individual reasons. Firstly, the mobile platform is the most effective way to reach the maximal amount of users. The majority of the first-world populous owns a smartphone and spends a reasonable amount of time using them through a multitude of purposes such as scheduling, conversation, and entertainment. Coupled with the lack of personal experience in this development area, it becomes evident that creating a mobile application was the most desirable choice for this project. Secondly, after hearing stories and reflections from my peers about their own humanitarian endeavors, their experiences sparked a desire to utilize this project as an opportunity to also create an impact. Thus, this project began to address the desire to solve a real world problem. Lastly, the first three years of the software engineering curriculum has been focused primarily on theoretical environments and projects. Most of these projects seemed to have no real world transferability. Therefore, having spent the time and effort to learn proper methods of software development, it would be remiss not to use these skills to train for future employment. This thesis began with a motivation to solve the time-consuming problem of finding health facilities that satisfy financial, insurance, and health needs. Two personal accounts of delayed proper medical services will be expanded upon later in this document. These experiences served as inspiration to delve further into the problem and determine if there was a solution to solve it. After months of exploring and planning, the project hit an impassible roadblock that deemed the project could no longer be continued. Within the remaining time constraints, changing the development area was not a viable solution to accomplish the intended goal. Thus, a new idea was conceived to assist those trying to cope with anxiety in today's world. One of the common recommendations for people suffering from anxiety is to write down their troubles with the intention of reflecting on them at a later point. This serves as a method to reason through the irrationality, enabling individuals to identify repetitive patterns over long periods of time. Physically writing down these reflections with pen and paper is no longer sufficient in this technological era. This is especially true for those wanting to retain their privacy or those without enough drive to consistently use this technique. The remaining months of this thesis were directed at planning and creating a prototype to address this.
ContributorsLaplace, Cecilia A. (Author) / Lindquist, Timothy (Thesis director) / Whitehouse, Richard (Committee member) / Software Engineering (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
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
Currently, students at Arizona State University are restricted to cards when using their college's local currency. This currency, Maroon and Gold dollars (M&G), is a primary source of meal plans for many students. When relying on card readers, students risk security and convenience. The security is risked due to the constant student id number on each card. A student's identification number never changes and is located on each card. If the student loses their card, their account information is permanently compromised. Convenience is an issue because, currently, students must make a purchase in order to see their current account balance. Another major issue is that businesses must purchase external hardware in order to use the M&G System. An online or mobile system would eliminate the need for a physical card and allow businesses to function without external card readers. Such a system would have access to financial information of businesses and students at ASU. Thus, the system require severe scrutiny by a well-trusted team of professionals before being implemented. My objective was to help bring such a system to life. To do this, I decided to make a mobile application prototype to serve as a baseline and to demonstrate the features of such a system. As a baseline, it needed to have a realistic, professional appearance, with the ability to accurately demonstrate feature functionality. Before developing the app, I set out to determine the User Interactions and User Experience designs (UI/UX) by conducting a series of informal interviews with local students and businesses. After the designs were finalized, I started implementation of the actual application in Android Studio. This creative project consists of a mobile application, a contained database, a GUI (Graphics User Interface) prototype, and a technical document.
ContributorsReigel, Justin Bryce (Author) / Bansal, Ajay (Thesis director) / Lindquist, Timothy (Committee member) / Software Engineering (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
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