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ADAPTIVE INTELLIGENT USER INTERFACES WITH EMOTION RECOGNITION
Title
ADAPTIVE INTELLIGENT USER INTERFACES WITH EMOTION RECOGNITION.
Creator
NASOZ, FATMA, Christine Lisetti, Dr L., University of Central Florida
Abstract / Description
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' 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...
Show moreThe focus of this dissertation is on creating Adaptive Intelligent User Interfaces to facilitate enhanced natural communication during the Human-Computer Interaction by recognizing users' 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' 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.
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Date Issued
2004
Identifier
CFE0000126, ucf:46201
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0000126
Modeling User Transportation Patterns Using Mobile Devices
Title
Modeling User Transportation Patterns Using Mobile Devices.
Creator
Davami, Erfan, Sukthankar, Gita, Gonzalez, Avelino, Foroosh, Hassan, Sukthankar, Rahul, University of Central Florida
Abstract / Description
Participatory sensing frameworks use humans and their computing devices as a large mobile sensing network. Dramatic accessibility and affordability have turned mobile devices (smartphone and tablet computers) into the most popular computational machines in the world, exceeding laptops. By the end of 2013, more than 1.5 billion people on earth will have a smartphone. Increased coverage and higher speeds of cellular networks have given these devices the power to constantly stream large amounts...
Show moreParticipatory sensing frameworks use humans and their computing devices as a large mobile sensing network. Dramatic accessibility and affordability have turned mobile devices (smartphone and tablet computers) into the most popular computational machines in the world, exceeding laptops. By the end of 2013, more than 1.5 billion people on earth will have a smartphone. Increased coverage and higher speeds of cellular networks have given these devices the power to constantly stream large amounts of data.Most mobile devices are equipped with advanced sensors such as GPS, cameras, and microphones. This expansion of smartphone numbers and power has created a sensing system capable of achieving tasks practically impossible for conventional sensing platforms. One of the advantages of participatory sensing platforms is their mobility, since human users are often in motion. This dissertation presents a set of techniques for modeling and predicting user transportation patterns from cell-phone and social media check-ins. To study large-scale transportation patterns, I created a mobile phone app, Kpark, for estimating parking lot occupancy on the UCF campus. Kpark aggregates individual user reports on parking space availability to produce a global picture across all the campus lots using crowdsourcing. An issue with crowdsourcing is the possibility of receiving inaccurate information from users, either through error or malicious motivations. One method of combating this problem is to model the trustworthiness of individual participants to use that information to selectively include or discard data.This dissertation presents a comprehensive study of the performance of different worker quality and data fusion models with plausible simulated user populations, as well as an evaluation of their performance on the real data obtained from a full release of the Kpark app on the UCF Orlando campus. To evaluate individual trust prediction methods, an algorithm selection portfolio was introduced to take advantage of the strengths of each method and maximize the overall prediction performance.Like many other crowdsourced applications, user incentivization is an important aspect of creating a successful crowdsourcing workflow. For this project a form of non-monetized incentivization called gamification was used in order to create competition among users with the aim of increasing the quantity and quality of data submitted to the project. This dissertation reports on the performance of Kpark at predicting parking occupancy, increasing user app usage, and predicting worker quality.
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Date Issued
2015
Identifier
CFE0005597, ucf:50258
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0005597
Applying the Technology Acceptance Model to Predict and Explain Elementary and Secondary Preservice Teachers' Continuance Behavioral Intentions and Pedagogical Usage of Twitter to build Professional Capital: A Structural Equation Modeling Inquiry
Title
Applying the Technology Acceptance Model to Predict and Explain Elementary and Secondary Preservice Teachers' Continuance Behavioral Intentions and Pedagogical Usage of Twitter to build Professional Capital: A Structural Equation Modeling Inquiry.
Creator
Gurjar, Nandita, Sivo, Stephen, Roberts, Sherron, Xu, Lihua, Vie, Stephanie, University of Central Florida
Abstract / Description
The purpose of this research study was to predict and explain elementary and secondary preservice teachers' continuance behavioral intentions and pedagogical usage of Twitter, a web based social networking, microblogging platform, to build professional growth and capital. The objective of the research study was to examine preservice teachers' beliefs associated with the specified constructs that formed the latent variables of the hypothesized research model; these latent variables were then...
Show moreThe purpose of this research study was to predict and explain elementary and secondary preservice teachers' continuance behavioral intentions and pedagogical usage of Twitter, a web based social networking, microblogging platform, to build professional growth and capital. The objective of the research study was to examine preservice teachers' beliefs associated with the specified constructs that formed the latent variables of the hypothesized research model; these latent variables were then measured with their associated indicators or manifest variables, and the relationship between the manifest variables was examined through the Structural Equation Modeling (SEM) process. A non-experimental empirical research study was conducted using the survey methodology; purposive, criterion referenced, sampling of elementary and secondary preservice teachers, N=379, was employed using social media platforms and intern listserv at a large Southeastern university. The final sample of N= 250 participants was determined through the process of regression imputation of elementary and secondary preservice teachers' survey responses. The results demonstrated that constructs of the extended Technology Acceptance Model showed significant goodness-of-fit indices and coefficients of determination after analyzing the data from the survey. Implications of this research contribute significantly toward teacher education and training by providing insights into the factors that impact the pedagogical use of Twitter, a web-based social networking and microblogging platform, for building professional capital in preservice teachers.
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Date Issued
2016
Identifier
CFE0006314, ucf:51551
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0006314
Pen-based Methods For Recognition and Animation of Handwritten Physics Solutions
Title
Pen-based Methods For Recognition and Animation of Handwritten Physics Solutions.
Creator
Cheema, Salman, Laviola II, Joseph, Hughes, Charles, Sukthankar, Gita, Hammond, Tracy, University of Central Florida
Abstract / Description
There has been considerable interest in constructing pen-based intelligent tutoring systems due to the natural interaction metaphor and low cognitive load afforded by pen-based interaction. We believe that pen-based intelligent tutoring systems can be further enhanced by integrating animation techniques. In this work, we explore methods for recognizing and animating sketched physics diagrams. Our methodologies enable an Intelligent Tutoring System (ITS) to understand the scenario and...
Show moreThere has been considerable interest in constructing pen-based intelligent tutoring systems due to the natural interaction metaphor and low cognitive load afforded by pen-based interaction. We believe that pen-based intelligent tutoring systems can be further enhanced by integrating animation techniques. In this work, we explore methods for recognizing and animating sketched physics diagrams. Our methodologies enable an Intelligent Tutoring System (ITS) to understand the scenario and requirements posed by a given problem statement and to couple this knowledge with a computational model of the student's handwritten solution. These pieces of information are used to construct meaningful animations and feedback mechanisms that can highlight errors in student solutions. We have constructed a prototype ITS that can recognize mathematics and diagrams in a handwritten solution and infer implicit relationships among diagram elements, mathematics and annotations such as arrows and dotted lines. We use natural language processing to identify the domain of a given problem, and use this information to select one or more of four domain-specific physics simulators to animate the user's sketched diagram. We enable students to use their answers to guide animation behavior and also describe a novel algorithm for checking recognized student solutions. We provide examples of scenarios that can be modeled using our prototype system and discuss the strengths and weaknesses of our current prototype.Additionally, we present the findings of a user study that aimed to identify animation requirements for physics tutoring systems. We describe a taxonomy for categorizing different types of animations for physics problems and highlight how the taxonomy can be used to define requirements for 50 physics problems chosen from a university textbook. We also present a discussion of 56 handwritten solutions acquired from physics students and describe how suitable animations could be constructed for each of them.
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Date Issued
2014
Identifier
CFE0005472, ucf:50380
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0005472
DEVELOPMENT OF A GRAPHICAL USER INTERFACE FOR CAL3QHC CALLED CALQCAD
Title
DEVELOPMENT OF A GRAPHICAL USER INTERFACE FOR CAL3QHC CALLED CALQCAD.
Creator
Gawalpanchi, Sheetal, Cooper, Charles, University of Central Florida
Abstract / Description
One of the major sources of air pollution in the United States metropolitan areas is due to automobiles. With the huge growth of motor vehicles and, greater dependence on them, air pollution problems have been aggravated. According to the EPA, nearly 95% of carbon monoxide (CO ) (EPA 1999) in urban areas comes from mobile sources, of which 51% is contributed by on road vehicles. It is well known fact that, carbon monoxide is one of the major mobile source pollutants and CO has detrimental...
Show moreOne of the major sources of air pollution in the United States metropolitan areas is due to automobiles. With the huge growth of motor vehicles and, greater dependence on them, air pollution problems have been aggravated. According to the EPA, nearly 95% of carbon monoxide (CO ) (EPA 1999) in urban areas comes from mobile sources, of which 51% is contributed by on road vehicles. It is well known fact that, carbon monoxide is one of the major mobile source pollutants and CO has detrimental effects on the human health. Carbon monoxide is the result of mainly incomplete combustion of gasoline in motor vehicles (FDOT 1996). The National Environmental Policy Act (NEPA) gives important considerations to the actions to be taken. Transportation conformity . The Clean Air Act Amendments (CAAA, 1970) was an important step in meeting the National Ambient Air Quality Standards In order to evaluate the effects of CO and Particulate Matter (PM) impacts based on the criteria for NAAQS standards, it is necessary to conduct dispersion modeling of emissions for mobile source emissions. Design of transportation engineering systems (roadway design) should take care of both the flow of the traffic as well as the air pollution aspects involved. Roadway projects need to conform to the State Implementation Plan (SIP) and meet the NAAQS. EPA guidelines for air quality modeling on such roadway intersections recommend the use of CAL3QHC. The model has embedded in it CALINE 3.0 (Benson 1979) – a line source dispersion model based on the Gaussian equation. The model requires parameters with respect to the roadway geometry, fleet volume, averaging time, surface roughness, emission factors, etc. The CAL3QHC model is a DOS based model which requires the modeling parameters to be fed into an input file. The creation of input the file is a tedious job. Previous work at UCF, resulted in the development of CALQVIEW, which expedites this process of creating input files, but the task of extracting the coordinates still has to be done manually. The main aim of the thesis is to reduce the analysis time for modeling emissions from roadway intersections, by expediting the process of extracting the coordinates required for the CAL3QHC model. Normally, transportation engineers design and model intersections for the traffic flow utilizing tools such as AutoCAD, Microstation etc. This thesis was to develop advanced software allowing graphical editing and coordinates capturing from an AutoCAD file. This software was named as CALQCAD. This advanced version will enable the air quality analyst to capture the coordinates from an AutoCAD 2004 file. This should expedite the process of modeling intersections and decrease analyst time from a few days to few hours. The model helps to assure the air quality analyst to retain accuracy during the modeling process. The idea to create the standalone interface was to give the AutoCAD user full functionality of AutoCAD tools in case editing is required to the main drawing. It also provides the modeler with a separate graphical user interface (GUI).
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Date Issued
2005
Identifier
CFE0000483, ucf:46364
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0000483