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The present study investigated the effects of reward structure and environmental conditions (i.e., context) on integrated motivation for an accounting task using 101 undergraduate accounting students. A computer-simulated task in which students were asked to estimate allowance for doubtful accounts was used to create and manipulate reward structure (i.e., performance-contingent vs. task-contingent) and context (i.e., self-determined vs. controlled). It was hypothesized that a self-determined context would create greater motivation than a controlled context when motivation was measured by response intensity, response persistence, integrated response intensity, and integrated response persistence. An ordinal interaction was also hypothesized such that in a self-determined context, performance-contingent rewards would create more motivation than task-contingent rewards, and in a controlled context, performance-contingent rewards would create less motivation than task-contingent rewards. Results indicated that response intensity, as measured by time on task, did not support the hypothesized main effect or the ordinal interaction; however, when self-reported effort was used as a measure of response intensity, support for both hypotheses was found. Similarly, when response persistence was measured by time on task, support for the hypotheses was not found; however, when number of problems worked during the free choice period was used to assess response persistence, hypothesized effects were supported. For integrated response intensity and persistence, support for the hypotheses was not found. / Ph. D.

Freshwater salinization syndrome is a rising threat globally which results in increased ion concentrations in inland freshwaters. This syndrome threatens healthy aquatic ecosystems and can alter the perception of the potability of finished drinking water. The Occoquan Reservoir, located in Northern Virginia, is a freshwater system that is facing rising salinization. Stakeholders for the reservoir have been convened to address these rising salinization concerns. Among these stakeholders, there are a variety of viewpoints on the significance of the salinization, which is preventing a high level of convergence around this threat. To assist in contextualizing this system, empirical cumulative distribution functions were generated from data gathered from various governmental sources and compared the reservoir's watershed and finished drinking water ion concentrations. These analyses show that the watershed and finished drinking water have some of the highest concentrations of sodium and chloride statewide. Additional investigations determined the trend of sodium increases in finished drinking water since the 1980s. Monte Carlo simulations were ran to determined whether there would be risks to human from ingesting this water should this trend continued. Results from these analyses greatly varied due to the wide range in drinking water ingestion rates. The purpose of these analyses is to assist with stakeholder convergence around the level of threat salinization poses to the reservoir and to initiate discussions of what an acceptable threshold for management could be. / Master of Science / Freshwater salinization syndrome is a rising threat globally which results in increased ion concentrations in inland freshwaters. This syndrome threatens healthy aquatic ecosystems and can alter the perception of the potability of finished drinking water. The Occoquan Reservoir, located in Northern Virginia, is a freshwater that is facing rising salinization. Stakeholders for the reservoir have been convened to address these concerns. Among the stakeholders, there are a variety of viewpoints on the significance of salinization. Various analyses were done to compare the sodium and chloride concentrations in the reservoir's watershed and in the finished drinking water with respective statewide levels. These analyses show that the watershed and finished drinking water have some of the highest concentrations of sodium and chloride statewide. Additional investigations were conducted to determine if there was a human health risk to consuming the finished drinking water. Results from this analysis were highly dependent on how much water an individual consumed. The purpose of these analyses is to assist with stakeholder convergence around the level of threat salinization poses to the reservoir and to initiate discussions of what an acceptable threshold for management could be.

JavaScript is a dynamic object-oriented programming language, which is designed with flexible programming mechanisms. JavaScript is widely used in developing sophisticated software systems, especially web applications. Despite of its popularity, there is a lack of software tools that support JavaScript for software engineering clients. Dataflow analysis approximates software behavior by analyzing the program code; it is the foundation for many software tools. However, several unique features of JavaScript render existing dataflow analysis techniques ineffective. Reflective constructs, generating code at runtime, make it difficult to acquire the complete program at compile time. Dynamic typing, resulting in changes in object behavior, poses a challenge for building accurate models of objects. Different functionalities can be observed when a function is variadic; the variance of the function behavior may be caused by the arguments whose values can only be known at runtime. Object constructors may be polymorphic such that objects created by the same constructor may contain different properties. In addition to object-oriented programming, JavaScript supports paradigms of functional and procedural programming; this feature renders dataflow analysis techniques ineffective when a JavaScript application uses multiple paradigms. Dataflow analysis needs to handle these challenges. In this work, we present an analysis framework and several dataflow analyses that can handle dynamic features in JavaScript. The first contribution of our work is the design and instantiation of the JavaScript Blended Analysis Framework (JSBAF). This general-purpose and flexible framework judiciously combines dynamic and static analyses. We have implemented an instance of JSBAF, blended taint analysis, to demonstrate the practicality of the framework. Our second contribution is an novel context-sensitive points-to analysis for JavaScript that accurately models object property changes. This algorithm uses a new program representation that enables partial flow-sensitive analysis, a more accurate object representation, and an expanded points-to graph. We have defined parameterized state sensitivity (i.e., k-state sensitivity) and evaluated the effectiveness of 1-state-sensitive analysis as the static phase of JSBAF. The third contribution of our work is an adaptive context-sensitive analysis that selectively applies context-sensitive analysis on the function level. This two-staged adaptive analysis extracts function characteristics from an inexpensive points-to analysis and uses learning-based heuristics to decide on an appropriate context-sensitive analysis per function. The experimental results show that the adaptive analysis is more precise than any single context-sensitive analysis for several programs in the benchmarks, especially for those multi-paradigm programs. / Ph. D.

This thesis presents the development and evaluation of an enhanced turn-key indoor positioning system (IPS) for tracking the mobility patterns of older adults in residential settings. The design of the IPS hardware and software focused on usability in the context of aging-in-place, while maintaining high data quality, reduced incidences of missing data, and elevated room detection accuracy, with the highest accuracy reaching 99.47%. By integrating positional data with IMU sensors, this system not only captures precise locations but also identifies activity states and contextual information, establishing a detailed profile of mobility patterns. A 'floor filter' in the data processing models was developed to address vertical alignment challenges commonly encountered in multi-story dwellings. This adjustment improved prediction accuracies, with an average accuracy increase of 3.33% to 6.28% across various models. Among these, the Multi-Layer Perceptron Neural Network (MLP NN) and Shallow Neural Network (SNN) exhibited the highest accuracies for user room location predictions. Furthermore, we demonstrated the practical application of these technologies in a real-world setting through pilot clinical studies involving older adults. This study not only validated the integration of IPS and IMU data but also facilitated the establishment of behavioral trends that are crucial for context-aware analysis. The system's ability to adapt to different indoor environments without extensive setup, alongside its proven accuracy and reliability in capturing detailed mobility and activity information, underscores its potential to enhance elderly care and support aging in place. By leveraging advanced machine learning models and innovative data processing techniques, this work contributes to the field by offering a robust, scalable solution for monitoring the mobility patterns of the elderly, thus paving the way for future healthcare applications designed to accommodate the complexities of aging populations. / Thesis / Master of Applied Science (MASc) / This project develops a system that helps track the location and movement of older adults in their homes to support their independence and reduce stress on healthcare services. It improves on current technology by providing more accurate tracking inside the home. The system uses sensors to monitor how active someone is and understands the context of their movements—like whether they are resting or moving around—which helps in assessing both their physical and mental well-being. The results show that this technology is effective in tracking daily activities and can help in providing better care for older adults.

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As computing becomes ubiquitous and intelligent, it is possible for systems to adapt their behavior based on information sensed from the situational context. However, determining the context space has been taken for granted in most ubiquitous applications, and so that context-adaptive systems often miss the situational factors that are most relevant to users. The mismatch between a system's computational model and users' mental model of the context may frustrate and disorient users. This thesis describes the CCM (cognitive context model)-based approach for eliciting individual cognitive views of a context-aware task and selecting an appropriate context space for context-aware computing. It captures the situational and cognitive context for each task, using a structural architecture in which individual participants use a context view to describe their situational perspective of the task. Clustering and optimization techniques are applied to analyze and integrate context views in CCM. Developers can use the optimization output to identify an appropriate context space, specify context-aware adaptation policies and resolve run-time policy conflicts. This approach simplifies the task of context elicitation, emphasizes individual variance in context-aware activity, and helps avoid user requirements misunderstanding.

As computing becomes ubiquitous and intelligent, it is possible for systems to adapt their behavior based on information sensed from the situational context. However, determining the context space has been taken for granted in most ubiquitous applications, and so that context-adaptive systems often miss the situational factors that are most relevant to users. The mismatch between a system's computational model and users' mental model of the context may frustrate and disorient users. This thesis describes the CCM (cognitive context model)-based approach for eliciting individual cognitive views of a context-aware task and selecting an appropriate context space for context-aware computing. It captures the situational and cognitive context for each task, using a structural architecture in which individual participants use a context view to describe their situational perspective of the task. Clustering and optimization techniques are applied to analyze and integrate context views in CCM. Developers can use the optimization output to identify an appropriate context space, specify context-aware adaptation policies and resolve run-time policy conflicts. This approach simplifies the task of context elicitation, emphasizes individual variance in context-aware activity, and helps avoid user requirements misunderstanding.

Context-aware computing is based on using knowledge about the current context. Interpretation of current context to an understandable knowledge is carried out by reasoning over context and in some cases by matching the current context with the desired context. In this thesis we concentrated on context matching issue in context-aware computing domain. Context matching can be done in various ways like it is done in other matching processes. Our matching approach is best matching in order to generate granular similarity results and not to be limited to Boolean values. We decided to use Ontology as the encoded domain knowledge for our matching method. Context matching method is related to the method that we represent context. We selected conceptual graphs to represent the context. We proposed a generic algorithm for context matching based on the ontological information that benefit from the conceptual graph theory and its advantages.