Development of simulation and machine learning solutions for social issues

When developing solutions for social issues, it can be difficult to evaluate the impact they may have without a real world implementation. This may not be possible for reasons such as resource, time, and monetary constraints. To resolve these issues, simulation and machine learning models can be used to mimic reality and provide a picture of how these solutions would fare. In Chapters 3 and 4, a deep learning approach to simulating homelessness populations in Canada is presented. This model would provide policy makers with a tool to test different solutions for this societal problem without the need to wait for approvals or funding from local officials. In addition to this solution, data enhancement techniques are presented as a comprehensive dataset on homeless population transitions for such a model to learn from does not exist. Lastly, Chapter 5 presents a transfer learning architecture to detect tents in satellite images. The motivation for this work was that “tent camps” are common for homeless populations to live in and by having a solution to detect these from images, policy makers can easily see where to focus resources such as shelters for example. Similar to the constraint present with the homelessness simulation, a comprehensive dataset on tents in satellite images does not exists. Therefore, this chapter also presents a solution to generate an comprehensive dataset for the architecture to learn from. The result of this thesis is developed solutions to social issues that utilize the power of machine learning and simulation models

East Lancashire Research 2008

East Lancashire Research 200

UA35/11 Student Honors Research Bulletin

The WKU Student Honors Research Bulletin is dedicated to scholarly involvement and student research. These papers are representative of work done by students from throughout the university. Bachert, Sara. Rational Portrayal of the Irrationational in The Pit and the Pendulum Bell, Suzanne. Early Secret Involvement of the United States Military in Cambodia Brock, Beth. The Informal Caregiving System: The Frail Elderlys\u27 Avenue of Choice Daniel, Janice. Child Sexual Abuse Johnson, Linda. International Telecommunications Trade with Japan Jones, LaMont. Ernie Pyle: Journalist Without Peer Kesserling, Marcia. Attitudes Toward the Need for Computer Literacy Lewis, Gloria. John Donne\u27s Attitude Toward Love Majdi, Lisa. The Effects of Robotization on the Workforce Page, Leslie. Larry Burrows and Modern War Photography Richardson, Vicky. Child Abuse: An Interview Riley, Grace. Elementary School Social Studies Instruction: Are Improvements in Order? Scariot, Linda. Parental Divorce and Childhood Emotional Disturbances Sharpe, Greg. Precipitation Patterns in Bowling Green, Kentucky Smith, Sandy. Religion and the Media: Alliance or War? Tuck, Janna and Karen Wiggins. Methylation and Confirmation of PG

Supporting Caregivers in Complex Home Care: Towards Designing a Voice User Interface

Despite significant advancements in the development of digital health tools and the rising provision of health care services in the home, information management and communication has yet to be standardized through digitization across caregiver teams in complex home care. With the increased risks of adverse events in dynamic and unpredictable home environments, there is a critical need to improve care inconsistencies and prevent communication breakdowns. An opportunity exists for digital health tools to support the standardization of information sharing processes in the home. However, designing digital tools to support complex home care is challenging when considering the uniqueness of patient conditions, the home environment, and caregiving team diversity. Adopting digital health tools in unregulated environments also induces a challenge for standardizing digitization in this complex domain. With advancements in natural language processing and speech recognition, the development of digital health interfaces that provide a natural interaction with information by voice has shown promise to support information management and communication and facilitate engagement with home care technology. The objective of this research is to build a foundation for the future development of a voice user interface or Voice Assistant (VA) to support caregivers in complex home care. The objectives are two-fold: (1) to understand the diverse caregiving experiences related to health information management and communication in complex home care and (2) evaluate the diverse perspectives of caregivers on the design of a VA to support these identified processes. Using a mixed-methods approach of semi-structured interviews and questionnaires with 22 caregivers across North America, this research contributes to understanding both the information and communication processes as well as the design considerations for integrating VA technology in complex home care by potential primary users. This thesis consists of three papers that describe the partial results of one study. One paper focuses on the semi-structured interviews with family caregivers of Children With Special Health Care Needs (CSHCN) to understand the processes involved with managing care in their home. The second paper focuses on the semi-structured interviews with family caregivers and hired caregivers of older adults in the same context. The third paper focuses on the semi-structured interviews and questionnaires with all participants about their expectations for the design of VAs in complex home care. This thesis captures the rich experiences of caregivers who are managing the coordination of care in complex home environments and the considerations for designing VA technology in this domain. The principal findings highlight similarities in caregiving processes and the nuanced complexities among caregiver populations that can inform the design and usability considerations of future digital health tools. There is also the potential for VA technology to provide utility for health information management and communication. However, considerations for functionality and the context of use may impact this innovation's diffusion. Future research should collectively examine home care from caregiving teams' perspectives and objectively measure human-information interaction with this technology in context-specific scenarios

Multiagent planning with Bayesian nonparametric asymptotics

Thesis: S.M., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (pages 95-105).Autonomous multiagent systems are beginning to see use in complex, changing environments that cannot be completely specified a priori. In order to be adaptive to these environments and avoid the fragility associated with making too many a priori assumptions, autonomous systems must incorporate some form of learning. However, learning techniques themselves often require structural assumptions to be made about the environment in which a system acts. Bayesian nonparametrics, on the other hand, possess structural flexibility beyond the capabilities of past parametric techniques commonly used in planning systems. This extra flexibility comes at the cost of increased computational cost, which has prevented the widespread use of Bayesian nonparametrics in realtime autonomous planning systems. This thesis provides a suite of algorithms for tractable, realtime, multiagent planning under uncertainty using Bayesian nonparametrics. The first contribution is a multiagent task allocation framework for tasks specified as Markov decision processes. This framework extends past work in multiagent allocation under uncertainty by allowing exact distribution propagation instead of sampling, and provides an analytic solution time/quality tradeoff for system designers. The second contribution is the Dynamic Means algorithm, a novel clustering method based upon Bayesian nonparametrics for realtime, lifelong learning on batch-sequential data containing temporally evolving clusters. The relationship with previous clustering models yields a modelling scheme that is as fast as typical classical clustering approaches while possessing the flexibility and representational power of Bayesian nonparametrics. The final contribution is Simultaneous Clustering on Representation Expansion (SCORE), which is a tractable model-based reinforcement learning algorithm for multimodel planning problems, and serves as a link between the aforementioned task allocation framework and the Dynamic Means algorithmby Trevor D. J. Campbell.S.M

Designing and Testing an Experimental Framework of Affective Intelligent Agents in Healthcare Training Simulations

A thesis submitted in partial fulfilment of the requirements of the University of Wolverhampton for the degree of Doctor of PhilosophyThe purpose of this study is to investigate how emotionally enabled virtual agents (VAs) in healthcare provision training simulations allow for a more effective level of understanding on how an emotionally enhanced scenario can affect different aspects of learning. This is achieved by developing virtual agents that respond to the user’s emotions and personality. The developed system also provides visual and auditory representations of the virtual agents’ state of mind. To enable the fulfilment of this purpose an experimental framework for incorporating emotional enhancements (concentrating on negative emotions such as stress, fear, and anxiety) into virtual agents in virtual training applications for healthcare provision is designed and implemented. The framework for incorporating emotional enhancements is designed based on previous research, on psychological theories (with input by experienced psychologists) and from input of experts in the area of healthcare provision. For testing the framework and answering the research question of this thesis the researcher conducted nine case studies. The participants were nursing students in the area of healthcare provision, and more specifically in the area of mental health, specialising in caring for patients with dementia. The results of the study showed that the framework and its implementation succeeded in providing a realistic learning experience, stimulated a better set of responses from the user, improved their level of understanding on how an emotionally enhanced scenario can affect the learning experience and helped them become more empathetic towards the person they cared for