What is mission critical in the hotel guest room: Examining in-room guest empowerment technologies

This study examined 18 in-room technologies and identified the ones perceived to be mission critical for the hotel guests. It also determined the differences in guest empowerment technology preferences and expectations across generations, purpose of travel, and travel frequency. Moreover, it investigated whether the quality of in-room technologies impacts guests\u27 decision in choosing a hotel. The data were collected through an online survey. A total of 508 people responded to the survey. An importance and performance analysis was utilized to identify the mission critical in-room technologies for the hotels. The analysis indicated that in-room movie on demand services, in-room wireless high speed internet service, high definition television content, in-room electronic temperature control, in-room electronic safe, connectivity panels, and all in one guest room control unit were perceived as being mission critical in-room technologies for hotel guests. The utilization of ANOVA and subsequent post-hoc tests showed that there were significant technology preference differences across the generations and travel frequency. Another important finding of this study was that a majority of respondents reported that the availability of new guest-room technologies would favorably impact their decision to select a hotel. The overall findings of this study provide information that would help hotel managers and owners to understand guests\u27 perceptions of and expectations for in-room technologies. These findings may possibly provide guidance for strategic purchasing, upgrading or implementing in-room technologies

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

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

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

ELVIS: Entertainment-led video summaries

© ACM, 2010. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in ACM Transactions on Multimedia Computing, Communications, and Applications, 6(3): Article no. 17 (2010) http://doi.acm.org/10.1145/1823746.1823751Video summaries present the user with a condensed and succinct representation of the content of a video stream. Usually this is achieved by attaching degrees of importance to low-level image, audio and text features. However, video content elicits strong and measurable physiological responses in the user, which are potentially rich indicators of what video content is memorable to or emotionally engaging for an individual user. This article proposes a technique that exploits such physiological responses to a given video stream by a given user to produce Entertainment-Led VIdeo Summaries (ELVIS). ELVIS is made up of five analysis phases which correspond to the analyses of five physiological response measures: electro-dermal response (EDR), heart rate (HR), blood volume pulse (BVP), respiration rate (RR), and respiration amplitude (RA). Through these analyses, the temporal locations of the most entertaining video subsegments, as they occur within the video stream as a whole, are automatically identified. The effectiveness of the ELVIS technique is verified through a statistical analysis of data collected during a set of user trials. Our results show that ELVIS is more consistent than RANDOM, EDR, HR, BVP, RR and RA selections in identifying the most entertaining video subsegments for content in the comedy, horror/comedy, and horror genres. Subjective user reports also reveal that ELVIS video summaries are comparatively easy to understand, enjoyable, and informative

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

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

Analysing user physiological responses for affective video summarisation

This is the post-print version of the final paper published in Displays. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2009 Elsevier B.V.Video summarisation techniques aim to abstract the most significant content from a video stream. This is typically achieved by processing low-level image, audio and text features which are still quite disparate from the high-level semantics that end users identify with (the ‘semantic gap’). Physiological responses are potentially rich indicators of memorable or emotionally engaging video content for a given user. Consequently, we investigate whether they may serve as a suitable basis for a video summarisation technique by analysing a range of user physiological response measures, specifically electro-dermal response (EDR), respiration amplitude (RA), respiration rate (RR), blood volume pulse (BVP) and heart rate (HR), in response to a range of video content in a variety of genres including horror, comedy, drama, sci-fi and action. We present an analysis framework for processing the user responses to specific sub-segments within a video stream based on percent rank value normalisation. The application of the analysis framework reveals that users respond significantly to the most entertaining video sub-segments in a range of content domains. Specifically, horror content seems to elicit significant EDR, RA, RR and BVP responses, and comedy content elicits comparatively lower levels of EDR, but does seem to elicit significant RA, RR, BVP and HR responses. Drama content seems to elicit less significant physiological responses in general, and both sci-fi and action content seem to elicit significant EDR responses. We discuss the implications this may have for future affective video summarisation approaches