Adaptive Intelligent User Interfaces With Emotion Recognition

The focus of this dissertation is on creating Adaptive Intelligent User Interfaces to facilitate enhanced natural communication during the Human-Computer Interaction by recognizing users\u27 affective states (i.e., emotions experienced by the users) and responding to those emotions by adapting to the current situation via an affective user model created for each user. Controlled experiments were designed and conducted in a laboratory environment and in a Virtual Reality environment to collect physiological data signals from participants experiencing specific emotions. Algorithms (k-Nearest Neighbor [KNN], Discriminant Function Analysis [DFA], Marquardt-Backpropagation [MBP], and Resilient Backpropagation [RBP]) were implemented to analyze the collected data signals and to find unique physiological patterns of emotions. Emotion Elicitation with Movie Clips Experiment was conducted to elicit Sadness, Anger, Surprise, Fear, Frustration, and Amusement from participants. Overall, the three algorithms: KNN, DFA, and MBP, could recognize emotions with 72.3%, 75.0%, and 84.1% accuracy, respectively. Driving Simulator experiment was conducted to elicit driving-related emotions and states (panic/fear, frustration/anger, and boredom/sleepiness). The KNN, MBP and RBP Algorithms were used to classify the physiological signals by corresponding emotions. Overall, KNN could classify these three emotions with 66.3%, MBP could classify them with 76.7% and RBP could classify them with 91.9% accuracy. Adaptation of the interface was designed to provide multi-modal feedback to the users about their current affective state and to respond to users\u27 negative emotional states in order to decrease the possible negative impacts of those emotions. Bayesian Belief Networks formalization was employed to develop the User Model to enable the intelligent system to appropriately adapt to the current context and situation by considering user-dependent factors, such as: personality traits and preferences

Predicting Variant Pathogenicity with Machine Learning

There are roughly 22,000 protein-coding genes in the human body, many of which play important roles in biological functions. The proteins fold in 3D space, and this is most often necessary for function. A genetic variant can disrupt the secondary structure of a protein (one aspect of structure) or eliminate a site important in protein-protein interaction or post-translational modification. The loss of function or deregulation can result in disease. Thus, there is great biomedical interest in identifying disease-causing single-nucleotide variants. We hypothesize that we can accurately predict variant pathogenicity. We used machine learning to predict the pathogenicity of a set of 28,369 single-nucleotide variants across 10 genes. The data are acquired from publicly available saturation mutagenesis data sets, which generate every possible amino acid substitution at every position in a protein. Our approach employs a support vector machine using linear, polynomial, and RBF kernel functions. The problem is implemented as a binary classification problem, where a label of 1 indicates a disease-causing variant and a label of 0 indicates a benign variant. The model predicts pathogenicity based on amino acid, post-translational modification, and secondary structure information. We cleaned and analyzed the data with custom Python scripts. Our results show average balanced accuracy scores for classifying pathogenicity of approximately 57.9%, 60.3%, and 60.3% for the linear, polynomial, and RBF kernels, respectively. Therefore, the model is an improvement over random guessing but has room for improvement.https://digitalscholarship.unlv.edu/durep_posters/1045/thumbnail.jp

Comparing the Administration of University Cooperative Extensions in the United States: A Case Analysis

For more than a century, cooperative extensions and the land-grant universities have translated and extended research-based knowledge and provided non-formal higher education to their communities. Today, more than 80% of the nation’s population are living in urban areas (The World Bank, 2015). Challenges facing diverse populations require cooperative extensions to collaborate and form partnerships to leverage resources and expertise. This brief explores the nation’s Cooperative Extension System, in particular the university cooperative extensions run by 1862 Land-Grant Universities. Researchers developed an intrinsic case study design to examine cooperative extensions in 15 states and interviewed leaders of the cooperative extensions to identify 1) how cooperative extensions collaborate with other institutions in and out of state; 2) whether cooperative extensions use local extension offices for student recruitment or fundraising; 3) funding sources of the cooperative extensions; and 4) whether cooperative extensions meet their goals. Common themes emerging from the study demonstrate a high-level of collaboration with other universities and faculty, and minimal use of local county offices for student recruitment and fundraising activities

Game Change: What Have We Learned? Pt. 2

Share Knowledge. Change Lives. Transform our Community. Our Mission: The Lincy Institute at UNLV conducts and supports research that focuses on improving Nevada\u27s health, education, and social services. Our Research Areas: Education, Health, Social Services, Information Technolog

Making Cooperative Extension Work for Southern Nevada: Fulfilling UNLV\u27s Urban Land Grant Mission

The Lincy Institute and Brookings Mountain West at UNLV are pleased to host a colloquium entitled, “Making Cooperative Extension Work for Southern Nevada: Fulfilling UNLV’s Urban Land Grant Mission.” The event will explore ways to rethink and reform County Cooperative Extension so that it is relevant to the modern metropolis that is the Las Vegas area. The colloquium will feature research presentations that examine County Cooperative Extension from social, economic, and operational perspectives

Environmental and Socio-Economic Stress in the Mountain West

This fact sheet examines data on environmental and socio-economic risk metrics including which metrics pose the most risk for Nevada counties.The data are retrieved from “System for the Triage of Risks from Environmental and Socio-Economic Stressors” created by the Massachusetts Institute of Technology (MIT) joint program on the science and policy of global change

America\u27s Best Small Cities in the Mountain West

This fact sheet reports the rankings of small cities in the Mountain West, based on perceived desirability for living, visiting, and investment. Resonance Consultancy’s “America’s Best Small Cities 2020,” report examines six key metrics: “People,” “Place,” “Product,” “Programming,” “Promotion,” and “Prosperity.” 11 small cities in the Mountain West are ranked within the top 100 in the United States

Rethinking Cooperative Extension in Southern Nevada

Cooperative Extension is a partnership funded by federal, state, and county governments that extends University of Nevada services to Nevadans. As the original branch of Nevada’s land-grant institution, the University of Nevada, Reno (UNR) has administered Cooperative Extension Service (CES) since the program’s inception over a century ago. However, as currently organized, CES has limited presence in Southern Nevada and it has not developed programming commensurate with Clark County’s tax contribution to the CES budget. We propose that CES in Southern Nevada be managed by the University of Nevada, Las Vegas (UNLV). As we show, UNLV is already the most connected and active non-profit organization in the region. The campus currently delivers a host of services and programs that are consistent with CES’s mission, despite receiving no direct funding to support these activities

Nevada Ballot Question 1: Reforming Higher Education Governance, 2020

This fact sheet explores voting data of 1,407,761 Nevadans who participated in the 2020 election through in-person early, mail-in, absentee, or election day voting. The Secretary of State publishes the outcomes of each race, and results are broken down by county. Voting data are displayed as raw vote totals and percent shares of the total vote. Also included are data on the number of voters who declined to vote on each of Nevada’s five ballot questions. These data are calculated by subtracting the “yes” and “no” votes for each ballot measure from the total number of ballots cast both statewide and in each county. Election results were last updated on December 17th, 2020 at 8:38am and have since been certified by the Nevada Secretary of State’s office