Existing machine learning and data mining techniques have difficulty in handling three characteristics of real-world data sets altogether in a computationally efficient way: (1) different data types with both categorical data and numeric data, (2) different variable relations in different value ranges of variables, and (3) unknown variable dependency.This dissertation developed a Partial-Value Association Discovery (PVAD) algorithm to overcome the above drawbacks in existing techniques. It also enables the discovery of partial-value and full-value variable associations showing both effects of individual variables and interactive effects of multiple variables. The algorithm is compared with Association rule mining and Decision Tree for validation purposes. The results show that the PVAD algorithm can overcome the shortcomings of existing methods. The second part of this dissertation focuses on knee point detection on noisy data. This extended research topic was inspired during the investigation into categorization for numeric data, which corresponds to Step 1 of the PVAD algorithm. A new mathematical definition of knee point on discrete data is introduced. Due to the unavailability of ground truth data or benchmark data sets, functions used to generate synthetic data are carefully selected and defined. These functions are subsequently employed to create the data sets for this experiment. These synthetic data sets are useful for systematically evaluating and comparing the performance of existing methods. Additionally, a deep-learning model is devised for this problem. Experiments show that the proposed model surpasses existing methods in all synthetic data sets, regardless of whether the samples have single or multiple knee points. The third section presents the application results of the PVAD algorithm to real-world data sets in various domains. These include energy consumption data of an Arizona State University (ASU) building, Computer Network, and ASU Engineering Freshmen Retention. The PVAD algorithm is utilized to create an associative network for energy consumption modeling, analyze univariate and multivariate measures of network flow variables, and identify common and uncommon characteristics related to engineering student retention after their first year at the university. The findings indicate that the PVAD algorithm offers the advantage and capability to uncover variable relationships.

The concept of multi-scale, heterogeneous modeling is well-known to be central in the complexities of natural and built systems. Therefore, whole models that have parts with different spatiotemporal scales are preferred to those specified using a monolithic modeling approach and tightly integrated. To build simulation frameworks that are expressive and flexible, model composability is crucial where a whole model's structure and behavior traits must be concisely specified according to those of its parts and their interactions. To undertake the spatiotemporal model composability, a breast cancer cells chemotaxis exemplar is used. In breast cancer biology, the receptors CXCR4+ and CXCR7+ and the secreting CXCL12+ cells are implicated in spreading normal and malignant cells. As discrete entities, these can be modeled using Agent-Based Modeling (ABM). The receptors and ligand bindings with chemokine diffusion regulate the cells' movement gradient. These continuous processes can be modeled as Ordinary Differential Equations (ODE) and Partial Differential Equations (PDE). A customized, text-based BrSimulator exists to model and simulate this kind of breast cancer phenomenon. To build a multi-scale, spatiotemporal simulation framework supporting model composability, this research proposes using composable cellular automata (CCA) modeling. Toward this goal, the Cellular Automata DEVS (CA-DEVS) model is used, and the novel Composable Cellular Automata DEVS (CCA-DEVS) modeling is proposed. The DEVS-Suite simulator is extended to support CA and CCA Parallel DEVS models. This simulator introduces new capabilities for controlled and modular run-time animation and superdense time trajectory visualization. Furthermore, this research proposes using the Knowledge Interchange Broker (KIB) approach to model and simulate the interactions between separate geo-referenced CCA models developed using the DEVS and Modelica modeling languages. To demonstrate the proposed model composability approach and its use in the extended DEVS-Suite simulator, the breast cancer cells chemotaxis and others have been studied. The BrSimulator is used as a proxy for evaluating the proposed model composability approach using an integrated DEVS-Suite and OpenModelica simulator. Simulation experiments are developed that show the composition of spatiotemporal ABM, ODE, and PDE models reproduce the behaviors of the same model developed in the BrSimulator.

Machine learning tutorials often employ an application and runtime specific solution for a given problem in which users are expected to have a broad understanding of data analysis and software programming. This thesis focuses on designing and implementing a new, hands-on approach to teaching machine learning by streamlining the process of generating Inertial Movement Unit (IMU) data from multirotor flight sessions, training a linear classifier, and applying said classifier to solve Multi-rotor Activity Recognition (MAR) problems in an online lab setting. MAR labs leverage cloud computing and data storage technologies to host a versatile environment capable of logging, orchestrating, and visualizing the solution for an MAR problem through a user interface. MAR labs extends Arizona State University’s Visual IoT/Robotics Programming Language Environment (VIPLE) as a control platform for multi-rotors used in data collection. VIPLE is a platform developed for teaching computational thinking, visual programming, Internet of Things (IoT) and robotics application development. As a part of this education platform, this work also develops a 3D simulator capable of simulating the programmable behaviors of a robot within a maze environment and builds a physical quadrotor for use in MAR lab experiments.

Preventive maintenance is a practice that has become popular in recent years, largely due to the increased dependency on electronics and other mechanical systems in modern technologies. The main idea of preventive maintenance is to take care of maintenance-type issues before they fully appear or cause disruption of processes and daily operations. One of the most important parts is being able to predict and foreshadow failures in the system, in order to make sure that those are fixed before they turn into large issues. One specific area where preventive maintenance is a very big part of daily activity is the automotive industry. Automobile owners are encouraged to take their cars in for maintenance on a routine schedule (based on mileage or time), or when their car signals that there is an issue (low oil levels for example). Although this level of maintenance is enough when people are in charge of cars, the rise of autonomous vehicles, specifically self-driving cars, changes that. Now instead of a human being able to look at a car and diagnose any issues, the car needs to be able to do this itself. The objective of this project was to create such a system. The Electronics Preventive Maintenance System is an internal system that is designed to meet all these criteria and more. The EPMS system is comprised of a central computer which monitors all major electronic components in an autonomous vehicle through the use of standard off-the-shelf sensors. The central computer compiles the sensor data, and is able to sort and analyze the readings. The filtered data is run through several mathematical models, each of which diagnoses issues in different parts of the vehicle. The data for each component in the vehicle is compared to pre-set operating conditions. These operating conditions are set in order to encompass all normal ranges of output. If the sensor data is outside the margins, the warning and deviation are recorded and a severity level is calculated. In addition to the individual focus, there's also a vehicle-wide model, which predicts how necessary maintenance is for the vehicle. All of these results are analyzed by a simple heuristic algorithm and a decision is made for the vehicle's health status, which is sent out to the Fleet Management System. This system allows for accurate, effortless monitoring of all parts of an autonomous vehicle as well as predictive modeling that allows the system to determine maintenance needs. With this system, human inspectors are no longer necessary for a fleet of autonomous vehicles. Instead, the Fleet Management System is able to oversee inspections, and the system operator is able to set parameters to decide when to send cars for maintenance. All the models used for the sensor and component analysis are tailored specifically to the vehicle. The models and operating margins are created using empirical data collected during normal testing operations. The system is modular and can be used in a variety of different vehicle platforms, including underwater autonomous vehicles and aerial vehicles.

Gamification is the process of adding game mechanics to non game activities, thus creating a more engaging environment. Loyals provides a gamification API which can be consumed to add Loyals (achievements) to any website, application, or mobile app. Loyals are used in two major ways: (1) to create an interactive environment where users are rewarded for completing tasks and (2) as contextual information useful for analyzing user interaction with the application. The interactive environment inspires users to continue using an application while the contextual information can be used for improving the application to draw in new loyal visitors, ad targeting, creating user profiles, and much more.

A new honors class created at Arizona State University utilizes a new "thinking" paradigm. The new paradigm is a problem solution using deductive logic and natural laws to replace the traditional acquisition and usage of detailed knowledge. When utilizing deductive logic, less time is required for students to learn, and students are able to resolve unique issues with minimal amounts of information. Students use their logic and processing skills to replace the traditional need of collecting large amounts of detailed information. The concepts taught in the class have come from the industry success of the Best Value (BV) approach developed by a leading research group at Arizona State University over the last 17 years. The research group identified the source of the industry's problem is due to the traditional business approach of management, direction and control (MDC). With over 1500 tests conducted, delivering $5.7B of services, with results showing: 30% decrease in cost, 30% increase in value, and customer satisfaction improvement by up to 140%, the Best Value (BV) approach has been identified as more efficient and can deliver better quality services than the traditional MDC approach. Through the research group's implementation of the new paradigm in higher education, the author identified a windfall effect that was able to give students understanding and an increased ability to cope with stressful situations, disease and extraordinary complications. It also exposed students to potentially harmful practices in their lives and has helped them to change. The study tested in K-12 proved potential value in exposing the paradigm to K-12 students, and what impact it may have on future professionals. The author's results include satisfaction rating of 9.5 (out of 10), increased career alignment by up to 113%, increased understanding of self by up to 70%, and a reduction of stress by up to 71%. The author's K-12 case studies aligned with the successful results shown in the industry and college classes run by the leading research group. The pattern of the new paradigm shows as resistance to it decreases, productivity, efficiency, processing speed, understanding, and effectiveness all increase.

Smartphones have become increasingly common over the past few years, and mobile games continue to be the most common type of application (Apple, Inc., 2013). For many people, the social aspect of gaming is very important, and thus most mobile games include support for playing with multiple players. However, there is a lack of common knowledge about which implementation of this functionality is most favorable from a development standpoint. In this study, we evaluate three different types of multiplayer gameplay (pass-and-play, Bluetooth, and GameCenter) via development cost and user interviews. We find that pass-and-play, the most easily-implemented mode, is not favored by players due to its inconvenience. We also find that GameCenter is not as well favored as expected due to latency of GameCenter's servers, and that Bluetooth multiplayer is the most well favored for social play due to its similarity to real-life play. Despite there being a large overhead in developing and testing Bluetooth and GameCenter multiplayer due to Apple's development process, this is irrelevant since professional developers must enroll in this process anyway. Therefore, the most effective multiplayer mode to develop is mostly determined by whether Internet play is desirable: Bluetooth if not, GameCenter if so. Future studies involving more complete development work and more types of multiplayer modes could yield more promising results.

The following is a case study composed of three workflow investigations at the open source software development (OSSD) based Apache Software Foundation (Apache). I start with an examination of the workload inequality within the Apache, particularly with regard to requirements writing. I established that the stronger a participant's experience indicators are, the more likely they are to propose a requirement that is not a defect and the more likely the requirement is eventually implemented. Requirements at Apache are divided into work tickets (tickets). In our second investigation, I reported many insights into the distribution patterns of these tickets. The participants that create the tickets often had the best track records for determining who should participate in that ticket. Tickets that were at one point volunteered for (self-assigned) had a lower incident of neglect but in some cases were also associated with severe delay. When a participant claims a ticket but postpones the work involved, these tickets exist without a solution for five to ten times as long, depending on the circumstances. I make recommendations that may reduce the incidence of tickets that are claimed but not implemented in a timely manner. After giving an in-depth explanation of how I obtained this data set through web crawlers, I describe the pattern mining platform I developed to make my data mining efforts highly scalable and repeatable. Lastly, I used process mining techniques to show that workflow patterns vary greatly within teams at Apache. I investigated a variety of process choices and how they might be influencing the outcomes of OSSD projects. I report a moderately negative association between how often a team updates the specifics of a requirement and how often requirements are completed. I also verified that the prevalence of volunteerism indicators is positively associated with work completion but what was surprising is that this correlation is stronger if I exclude the very large projects. I suggest the largest projects at Apache may benefit from some level of traditional delegation in addition to the phenomenon of volunteerism that OSSD is normally associated with.

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.

Software Managed Manycore (SMM) architectures - in which each core has only a scratch pad memory (instead of caches), - are a promising solution for scaling memory hierarchy to hundreds of cores. However, in these architectures, the code and data of the tasks mapped to the cores must be explicitly managed in the software by the compiler. State-of-the-art compiler techniques for SMM architectures require inter-procedural information and analysis. A call graph of the program does not have enough information, and Global CFG, i.e., combining all the control flow graphs of the program has too much information, and becomes too big. As a result, most new techniques have informally defined and used GCCFG (Global Call Control Flow Graph) - a whole program representation which captures the control-flow as well as function call information in a succinct way - to perform inter-procedural analysis. However, how to construct it has not been shown yet. We find that for several simple call and control flow graphs, constructing GCCFG is relatively straightforward, but there are several cases in common applications where unique graph transformation is needed in order to formally and correctly construct the GCCFG. This paper fills this gap, and develops graph transformations to allow the construction of GCCFG in (almost) all cases. Our experiments show that by using succinct representation (GCCFG) rather than elaborate representation (GlobalCFG), the compilation time of state-of-the-art code management technique [4] can be improved by an average of 5X, and that of stack management [20] can be improved by an average of 4X.