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This thesis is a description and analysis of work I produced at the University of New Orleans during my Graduate studies. My work centers on the theme of tension, human interaction, and the vulnerability and dominance of those interactions. I create paintings, drawings, and prints to articulate my theme of tension. These works of art are meant to describe the feeling of tension through my mark making.

With Home Virtual Assistant (HVA) becoming popular, it has entered many families. This study aims to understand how novice users explore the capabilities of HVAs and the factors influence the users’ discovery process. The researcher used a study inspired by diary study, combining survey and interview to collect the data, along with dialogue history that contains all the activities between the user and the device. The results reveal the activities the users engage in and the factors of the HVA device and the users that influence the discovery process. Three themes emerged from the data, they are: the activities participant engaged to learn the capabilities of Echo Dot, the influence of Echo Dot being screen-less and factors of participants that influenced the initial use. Based on the results, the researcher discusses the current issues of the Echo Dot’s design and suggests potential improvement for the HVAs.

ABSTRACT Providing good customer service is crucial to many commercial organizations. There are different means through which the service can be provided, such as Ecommerce, call centres or face-to-face. Although some service is provided through electronic or telephone-based interaction, it is common that the service is provided through human agents. In addition, many customer service interactions also involve a computer, for example, an information system where a travel agent finds suitable flights. This thesis seeks to understand the three channels of customer service interactions between the agent, customer and computer: Customer-Agent-Computer Interaction (CACI). A set of ethnographic studies were conducted at call centres to gain an initial understanding of CACI and to investigate the customer-computer channel. The findings revealed that CACI is more complicated than traditional CHI, because there is a second person, the customer, involved in the interaction. For example, the agent provides a lot of feedback about the computer to the customer, such as, I am waiting for the computer . Laboratory experiments were conducted to investigate the customer-computer channel by adding non-verbal auditory feedback about the computer directly to the customers. The findings showed only a small insignificant difference in task completion time and subjective satisfaction. There were indications that there was an improvement in flow of communication. Experiments were conducted to investigate how the two humans interact over two different communication modes: face-to-face and telephone. Findings showed that there was a significantly shorter task completion time via telephone. There was also a difference in style of communication, with face-to-face having more single activities, such as, talking only, while in the telephone condition there were more dual activities, for instance talking while also searching. There was only a small difference in subjective satisfaction. To investigate if the findings from the laboratory experiment also held in a real situation and to identify potential improvement areas, a series of studies were conducted: observations and interviews at multiple travel agencies, one focus group and a proof of concept study at one travel agency. The findings confirmed the results from the laboratory experiments. A number of potential interface improvements were also identified, such as, a history mechanism and sharing part of the computer screen with the customer at the agent's discretion. The results from the work in this thesis suggest that telephone interaction, although containing fewer cues, is not necessarily an impoverished mode of communication. Telephone interaction is less time consuming and more task-focused. Further, adding non-verbal auditory feedback did not enhance the interaction. The findings also suggest that customer service CACI is inherently different in nature and that there are additional complications with traditional CHI issues.

This thesis discusses a system that allows an operator to use two unmanned aerial vehicles (UAVs) to search an area. Prior work accomplished this in separate survey and search missions, and this work combines those two missions into one. The user conducts a search by selecting an area to survey, and the first drone flies above it, providing up to date information about the area. Points of interest (POI) are then marked by the user and investigated by the second drone. This system assumes a static and known obstacle map, and segmenting the environment during the missions leaves potential for future work. Both drones are equipped with cameras that stream video for the user to observe. A custom graphical user interface (GUI) was created to allow for the drones to be controlled. In addition to marking a search area and POI, the user can pause the drone and delete or add new POI to change the mission mid-flight. Both drones are commanded remotely by a ground station (GCS), leaving only low-level control to the onboard computers. This ground station uses a nearest neighbor solution to the travelling salesman problem and a wavefront path planner to create a path for the low altitude drone. The software architecture is based on the Robot Operating System (ROS), and the GCS uses the MAVLink messaging protocol to communicate with the drones. In addition to the system design, this paper discusses UAV human interaction and how it is applied to this system. / Master of Science / This thesis discusses a system that allows an operator to use two drones to search an area. Prior work accomplished this in separate survey and search missions, and this work combines those two missions into one. The user conducts a search by selecting an area to survey, and the first drone flies above it, providing up to date information about the area. Points of interest (POI) are then marked by the user and investigated by the second drone. This system assumes that obstacles in the environment are static and already known. Both drones are equipped with cameras that stream video for the user to observe. A custom graphical user interface (GUI) was created to allow for the drones to be controlled. In addition to providing the initial mission for the drones, the user can also change the mission mid-flight. Both drones are commanded remotely by a separate computer, leaving only very basic control to the drones. This ground station uses a simple path planner to create a path for the low altitude drone to avoid obstacles. The software architecture is based on the Robot Operating System (ROS), and the GCS uses the MAVLink messaging protocol to communicate with the drones. In addition to the system design, this UAV human interaction and how it is applied to this system.

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Category-level object recognition is a fundamental capability for the potential use of robots in the assistance of humans in useful tasks. There have been numerous vision-based object recognition systems yielding fast and accurate results in constrained environments. However, by depending on visual cues, these techniques are susceptible to object variations in size, lighting, rotation, and pose, all of which cannot be avoided in real video data. Thus, the task of object recognition still remains very challenging. My thesis work builds upon the fact that robots can observe humans interacting with the objects in their environment. We refer to the set of objects, which can be involved in the interaction as `interactionable' objects. The interaction of humans with the `interactionable' objects provides numerous nonvisual cues to the identity of objects. In this thesis, I will introduce a flexible object recognition approach called Multiple-Cue Object Recognition (MCOR) that can use multiple cues of any predefined type, whether they are cues intrinsic to the object or provided by observation of a human. In pursuit of this goal, the thesis will provide several contributions: A representation for the multiple cues including an object definition that allows for the flexible addition of these cues; Weights that reflect the various strength of association between a particular cue and a particular object using a probabilistic relational model, as well as object displacement values for localizing the information in an image; Tools for defining visual features, segmentation, tracking, and the values for the non-visual cues; Lastly, an object recognition algorithm for the incremental discrimination of potential object categories. We evaluate these contributions through a number of methods including simulation to demonstrate the learning of weights and recognition based on an analytical model, an analytical model that demonstrates the robustness of the MCOR framework, and recognition results on real video data using a number of datasets including video taken from a humanoid robot (Sony QRIO), video captured from a meeting setting, scripted scenarios from outside universities, and unscripted TV cooking data. Using the datasets, we demonstrate the basic features of the MCOR algorithm including its ability to use multiple cues of different types. We demonstrate the applicability of MCOR to an outside dataset. We show that MCOR has better recognition results over vision-only recognition systems, and show that performance only improves with the addition of more cue types.