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A significant change that employees in the United States have experienced because of the COVID-19 pandemic is the rise of remote work. There are many benefits of remote work, including increased performance and productivity. However, many remote workers struggle to separate work and life, leading to increased work-life conflict. Parents working remotely during the pandemic were the most likely to report challenges keeping work and life separate. The goal of this study was to better understand how parents’ work-life boundaries have changed during the pandemic. Based on interviews with 16 mothers and 16 fathers, this study examined how parents’ preferences for keeping work and life separate have adjusted and what factors affect their work and life. This research also looked at how mothers and fathers differ in their strategies to navigate between work and life when working remotely. This research uncovered six themes on how parents managed their work-life boundaries: two mindset shifts and four work-life separation strategies. Parents adapted their mindsets by redefining their priorities and setting realistic work and family expectations. The four boundary management strategies were turning off technology, sticking to a schedule, designating a home office space, and using a door-closed policy.
This dissertation analyzes performance distribution, the financial impact of star performers, and how the researcher's discipline moderates the relationship between individual performance and the value of external funding at a private R1 institution. While publications have traditionally served as a metric for faculty success at research institutions, there needs to be more knowledge regarding the role of star performers in securing external funding. Data drawn from the institution's internal application system encompassed a sample size of 7,213 proposals submitted by faculty members over five years. Utilizing the Dpit package from the Comprehensive R Archive Network (CRAN), results indicate that a power law distribution offers a better fit than a normal distribution for modeling star performance, with a significant portion of generated value concentrated among a select group of star performers. Furthermore, the research demonstrates that an organization's strategic core competence moderates the individual performance of researchers and the overall value derived from research initiatives.
Reports of 2022 employment rates demonstrate that while 65.4% of adults without disabilities are employed, only 21.3% of adults with disabilities are employed (U.S. Bureau of Labor Statistics, 2023). In 2022, data indicated 30% of adults with disabilities who were employed worked parttime jobs, nearly twice as much as those without disabilities (16%). Yet, research indicates that adults with disabilities can be integral parts of the workforce (Lipscomb et al., 2017; Lombardi et al., 2022; Luecking & Fabian, 2000; Newman et al., 2011). Researchers have reported that employees with disabilities are unable to maintain employment often due to difficulty fitting in socially at the workplace (Brickey et al., 1985; Butterworth & Strauch, 1994; Chadsey, 2007; Greenspan & Shoultz, 1981; Kochany & Keller, 1981; Wehman et al., 1982). Since 2009, social skills performance has been identified as a predictor of postschool success (Mazzotti et al., 2016, 2021; Test et al., 2009) meaning that students with disabilities who exited high school were more likely to participate in postschool employment (Benz et al., 1997; Roessler et al., 1990; Test et al., 2009).
Social skills challenges have been identified as one potential barrier to obtaining and maintaining employment for adults with disabilities (Bury et al., 2020; Kochman et al., 2017; Parker et al., 2018). While there is a strong link between social skills performance and success in the workplace, there are limited data on the interventions to maintain teaching these skills to adults with disabilities. Researchers have used different methods to create different intervention or strategies to help individuals with disabilities improve their social skills including specific curricula such as Conversing with Others and WAGES (Lu et al., 2020; Murray & Doren, 2013), instructional models such as the SDCDM and SDLMI (Dean et al., 2021; Shogren et al., 2018), in-ear coaching (Gilson & Carter, 2016), and video modeling (Bross et al., 2019, 2020; Whittenburg et al., 2022); however, these studies do not focus on social interactions between adults with disabilities and their coworkers to increase behaviors, rather communicating with coworkers or communicating about work tasks.
The purpose of this study was to evaluate the effects of a video modeling and a visual support intervention package on appropriate coworker social skills in the workplace for young adults with disabilities. I also collected data on participants’, coworkers’, and the employer’s perceptions of this study's goals, procedures, and outcomes Results of this study indicated a functional relation for one of the two participants. In addition, the participants, employer, and coworkers found the intervention to be socially valid across most measures. The dissertation includes a review of the literature, methods, discussion of each research question, study limitations, directions or future research, and implications.
A key component of team performance is participation among group members. One widespread organizational function that provides a stage for participation is the workplace meeting. With the shift to remote work, roughly half of all meetings are now conducted virtually (Cisco, 2022). In this new context, meeting participation is mediated through technology – which presents new challenges and opportunities for meeting leaders and attendees. One encouraging opportunity that can elevate meeting participation is the use of written chat during virtual meetings. Text-based chat offers a second avenue of participation during a meeting, where attendees can synchronously contribute to the conversation through writing, in a manner that typically does not disrupt the verbal discussion. The current study leverages research and theory on (a) individual differences (i.e., status characteristics theory), (b) employee perceptions of psychological safety and (c) work group participation in a virtual context to explore potential antecedents of engaging in chat during virtual meetings. Results suggest women and those high in job level participate in the meeting chat more frequently than their counterparts. We find perceptions of psychological safety moderate the relationship between job level and chat participation. Employees low in job level who have high perceptions of psychological safety participate in chat more frequently compared to their counterparts who report low perceptions of psychological safety. Results contribute to our understanding of written communication in virtual meetings, unpacking the individual differences in chat participation in a technology-mediated space. Further, our findings enhance our understanding of psychological safety; and how creating a psychologically safe environment can influence one’s method of participation in virtual meetings.
In the rapidly evolving landscape of intelligent transportation, the pressing need for real-time Artificial intelligence-based trajectory prediction and anomaly detection in highway scenarios is paramount. Ensuring the safety of highway workers, optimizing traffic flow, and enhancing surveillance mechanisms necessitate advancements tailored for embedded-edge platforms. This dissertation responds to these imperatives by developing a lightweight deep learning model that transitions from traditional LSTMs to leverage the efficiency of Agile Temporal Convolutional Networks, achieving streamlined computational requirements without sacrificing accuracy. An extensive vehicle trajectory dataset is presented, capturing a diverse range of driving scenes and road configurations from 1.6 million frames. To further the field, an innovative vehicle trajectory prediction model is introduced, employing attention-based mechanisms and outperforming existing benchmarks. The research culminates in an integrated AI pipeline optimized for real-time anomaly detection on highways. This system, synergized with a pioneering anomaly-specific dataset, sets new benchmarks in highway safety and surveillance, showcasing the potential of AI-driven solutions in addressing contemporary transportation challenges.
This dissertation presents a comprehensive exploration of innovative approaches and systems at the intersection of edge computing, deep learning, and real-time video analytics, with a focus on real-world computer vision for the Artificial Intelligence of Things (AIoT). The research comprises four distinct articles, each contributing to the advancement of AIoT systems, intelligent surveillance, lightweight human pose estimation, and real-world domain adaptation for person re-identification.
The first article, REVAMP2T: Real-time Edge Video Analytics for Multicamera Privacy-aware Pedestrian Tracking, introduces REVAMP2T, an integrated end-to-end IoT system for privacy-built-in decentralized situational awareness. REVAMP2T presents novel algorithmic and system constructs to push deep learning and video analytics next to IoT devices (i.e. video cameras). On the algorithm side, REVAMP2T proposes a unified integrated computer vision pipeline for detection, reidentification, and racking across multiple cameras without the need for storing the streaming data. At the same time, it avoids facial recognition, and tracks and reidentifies pedestrians based on their key features at runtime. On the IoT system side, REVAMP2T provides infrastructure to maximize hardware utilization on the edge, orchestrates global communications, and provides system-wide re-identification, without the use of personally identifiable information, for a distributed IoT network. For the results and evaluation, this article also proposes a new metric, Accuracy•Efficiency (Æ), for holistic evaluation of AIoT systems for real-time video analytics based on accuracy, performance, and power efficiency. REVAMP2T outperforms current state-of-the-art by as much as thirteen-fold Æ improvement.
The second article, Ancilia: Scalable Intelligent Video Surveillance for the
Artificial Intelligence of Things, presents an end-to-end scalable intelligent video
surveillance system tailored for the Artificial Intelligence of Things. Ancilia brings
state-of-the-art artificial intelligence to real-world surveillance applications while respecting ethical concerns and performing high-level cognitive tasks in real-time. Ancilia aims to revolutionize the surveillance landscape, to bring more effective, intelligent, and equitable security to the field, resulting in safer and more secure communities without requiring people to compromise their right to privacy.
The third article, EfficientHRNet: Efficient and Scalable High-Resolution Networks for Real-Time Multi-Person 2D Human Pose Estimation, focuses on the increasing demand for lightweight multi-person pose estimation, a vital component of emerging smart IoT applications. Existing algorithms tend to have large model sizes and intense computational requirements, making them ill-suited for real-time applications and deployment on resource-constrained hardware. Lightweight and real-time approaches are exceedingly rare and come at the cost of inferior accuracy. This article presents EfficientHRNet, a family of lightweight multi-person human pose estimators that are able to perform in real-time on resource-constrained devices. By unifying recent advances in model scaling with high-resolution feature representations, EfficientHRNet creates highly accurate models while reducing computation enough to achieve real-time performance. The largest model is able to come within 4.4% accuracy of the current state-of-the-art, while having 1/3 the model size and 1/6 the computation, achieving 23 FPS on Nvidia Jetson Xavier. Compared to the top real-time approach, EfficientHRNet increases accuracy by 22% while achieving similar FPS with 1 the power. At every level, EfficientHRNet proves to be more computationally efficient than other bottom-up 2D human pose estimation approaches, while achieving highly competitive accuracy.
The final article introduces the concept of R2OUDA: Real-world Real-time Online Unsupervised Domain Adaptation for Person Re-identification. Following the popularity of Unsupervised Domain Adaptation (UDA) in person reidentification, the recently proposed setting of Online Unsupervised Domain Adaptation (OUDA) attempts to bridge the gap towards practical applications by introducing a consideration of streaming data. However, this still falls short of truly representing real-world applications. The R2OUDA setting sets the stage for true real-world real-time OUDA, bringing to light four major limitations found in real-world applications that are often neglected in current research: system generated person images, subset distribution selection, time-based data stream segmentation, and a segment-based time constraint. To address all aspects of this new R2OUDA setting, this paper further proposes Real-World Real-Time Online Streaming Mutual Mean-Teaching (R2MMT), a novel multi-camera system for real-world person re-identification. Taking a popular person re-identification dataset, R2MMT was used to construct over 100 data subsets
and train more than 3000 models, exploring the breadth of the R2OUDA setting to understand the training time and accuracy trade-offs and limitations for real-world
applications. R2MMT, a real-world system able to respect the strict constraints of the proposed R2OUDA setting, achieves accuracies within 0.1% of comparable OUDA methods that cannot be applied directly to real-world applications.
Collectively, this dissertation contributes to the evolution of intelligent surveillance, lightweight human pose estimation, edge-based video analytics, and real-time unsupervised domain adaptation, advancing the capabilities of real-world computer vision in AIoT applications.
Peak load forecasting is crucial for reliable and effective grid operation. The day-to-day operation of the power grid requires load scheduling and dispatches of different energy resources including Energy Storage System and Demand Side Management programs. An effective implementation of these peak-shaving strategies relies heavily on when the peak demand occurs. Hence, forecasting the timing of peak load is as important as forecasting its magnitude.
A review of relevant literature indicates that there is no inclusive study on the topic of peak timing forecasting. This research aims to bridge the gap between industry requirements and academic research by addressing some key questions. First, the study defines the different forms of peak timing problems that arise in grid operation. Next, we investigate the problem of how we measure the peak timing forecast errors. The research critically reviews error measures used in the literature for peak timing forecasting. Based on the findings five new application-specific error measures are proposed. The research then focuses on one of the manifestations of the peak timing problem, that is, forecasting daily peak hours.
We analyzed the accuracy of peak hour forecasts from a state-of-the-art hourly load forecasting model and set it as the benchmark. The model selection process using different peak timing errors and load shape errors is investigated. Furthermore, two different frameworks for peak hour forecasting have been developed. The effectiveness of the proposed frameworks is empirically demonstrated in two case studies. The first case study is from a medium-sized Utility in the U.S. and the second one is from ISO New England. The proposed models demonstrate improved forecast results on the benchmark model by 12-16% in the test years of the two case studies. Additionally, when the models are only evaluated on the critical days with very high demands, they outperform the benchmark by 25-53%. Findings from this study emphasize the importance of developing explicit models for peak hour forecasting by analyzing the key determinants that vary with geographical location and regional factors.
Young children are assessed to meet federal mandates and inform policy decisions, provide teachers with useful information to make instructional decisions and set reasonable learning goals, and facilitate communication with families. While young children are frequently assessed using whole-child assessments which often yield criterion-referenced score interpretations, norm-referenced score interpretations can help teachers understand relative performance and set reasonable goals for growth. Although researchers have provided validity evidence for both criterion- and norm-referenced score interpretations for one widely used early childhood assessment, GOLD®, current national normative scores lack precision for several reasons, including the use of two-time-point and cross-sectional data. To improve estimates, a nationally representative sample of assessment records from 18,000 children ages birth through kindergarten was fitted to a series of hierarchical linear models (HLMs) to establish normative estimates conditional on months of age or instruction. Secondary study purposes included making inferences about the nature of growth from birth through kindergarten, providing evidence of the most effective time metric for modeling developmental growth, and examining the relationship between child-level characteristics and normative scores. Results indicated that a) HLMs provide reasonably valid normative ability and growth estimates, b) developmental growth, as measured by GOLD®, from birth through kindergarten is non-linear, c) the most effective time metric depends on the age band and domain of development, and d) child-level characteristics, including, race/ethnicity, gender, and primary language are associated with significantly different patterns of preliminary performance and growth for children who are one- or two-years of age or older.
The Housing Act of 1949 set its goals to revitalize American cities and provide adequate housing and suitable living environments for families. Although this goal has been achieved for some Americans, the lack of affordable housing and homelessness continues to be a serious public policy issue. Chronic homelessness, after declining for years, is on the rise. As a remedy, many cities have adopted the Housing First model, as part of their Continuum of Care, to place people who are homeless into housing. The purpose of this study was to learn more about the locations of Housing First placements and assess their proximity to supportive services in Charlotte, North Carolina. Using geospatial analysis, the findings revealed that housing placements were quite concentrated, with the majority being located in just six zip codes, where median rents were well below the city’s average and poverty rates were higher. Residents were also disproportionately Black or Hispanic. Although most housing placements were close to bus stops, they were not close to other services (e.g., grocery stores, pharmacies, hospitals, schools, or recreation areas). Moreover, nonprofit service providers responding to an online survey acknowledged that transportation, staffing, and funding for supportive services could be better. By adopting Housing First and implementing other efforts to increase affordable housing, Charlotte has demonstrated a clear interest in preventing and ending homelessness. Yet, there are still opportunities to do things differently by learning from other communities, which have adopted a range of creative and innovative policy solutions.
The effects of the COVID-19 pandemic changed the way education transpired for teachers and learners worldwide. Widespread virtual learning brought deeper academic and social inequities among K-12 diverse learners to light. Multilingual learners and their teachers were no exception. Research has yet to deeply explore the topic of ELD teachers’ experiences with advocating for their multilingual students during this unique time in educational history, as well as the lessons they learned during the pandemic that inform their advocacy work today. This phenomenological study used in-depth, semi-structured interviews to investigate these experiences. Potential implications for this study include teacher preparation, professional development, as well as policy-making decisions surrounding advocacy needs for multilingual learners.
