A software system is an aggregate of communicating modules, and there are several different types of communication among these modules (direct, indirect, and global). Therefore, understanding the interfaces among these modules can characterize the system and are a major factor in the system's complexity. These interfaces could possibly also show and predict inadequacies in the reliability and maintenance of a system. Interfaces are defined early in the development life cycle at a detailed or high level design stage. Knowing that these interfaces exist and their structure leads us to measure them for an indication of the designed interface complexity. This designed interface complexity can then be utilized for software quality assurance by allowing users to choose from among several designs. With data provided by an Ada software developer, the interface complexity metrics correlated with established metrics, but also found complex interfaces that established metrics missed. / Ph. D.

Research has shown that many antihistamines produce sedative effects as well as impair psychomotor performance. Performance testing of antihistamines, however, has not produced reliable evidence that there are behavioral effects at therapeutic dose levels. Therefore, the objective of this research was to determine whether a complex cognitive and motor task (memory search and tracking combination) showed a performance deterioration under the influence of two antihistamines (benadry| and hismanal) and to determine if the chosen task was of sufficient sensitivity to register decrements in performance at therapeutic dose levels of either of these two antihistamines. Thirty male subjects were divided into five groups of six subjects each. Each of the five groups was tested one day per week for three consecutive weeks. All Subjects received all three treatments (two antihistamines and a placebo) over the course of the test sessions. Order effect of the drug administration was counterbalanced. Analyses of variance showed that benadryl impaired performance on both components of the task as expected. Performance under hismanal did not vary significantly from the placebo. Post hoc testing further revealed an expected significant effect of benadryl three hours following ingestion for three out of four dependent variables. Again, hismanal effects did not vary significantly from those of the placebo. Hence, the memory/tracking combination task registered an expected performance impairment by benadryl which implies sufficient sensitivity of the task to register decrements. Also, hismanal displayed an expected lower incidence of behavioral effects as measured by response time and tracking error, which implies hismanal's usefulness in facilitating normal performance. / Master of Science

It is well established in developmental science that 14-month-old infants have significant difficulty associating pairs of objects with pairs of words that differ by a single phoneme (i.e., minimal pairs). This study used a traditional switch procedure in two experimental conditions (i.e., no face versus face with shifting gaze) to habituate infants with objects and minimal pair labels. Additionally, infants participated in a joint attention task and parents completed questionnaires related to family demographics and infant health and development, to compare to switch task performance. It was expected that infants' difficulty with minimal pair associative learning would be replicated in the no face condition. It was also predicted that the addition of a female face and the cues it could provide would abate the challenge that this task typically presents. As a group, infants' performances in the two conditions were not significantly different from each other and were not significantly different from chance. Analyses explored the relations between switch performance, joint attention task performance and questionnaire data, resulting in a significant correlation between performance in the face condition of the switch task and number of ear infections (r = .62, p < .05). Taken together, the addition of a female face with shifting gaze to a challenging word learning task does not sufficiently attenuate the cognitive load created by the task. The implications of these results are discussed further. / Master of Science

Research on the autonomic specificity of emotion has spanned several decades. Even though considerable evidence exists for supporting autonomic specificity for discrete emotion states (Kreibig, 2010), there is still an active debate, and conflicting explanations, for these findings (Quigley and Barrett, 2014). There have been several studies employing multivariate pattern classification analytic techniques and calls for those types of studies are still prevalent (Kragel and LaBar, 2014). Although many studies have explored the autonomic specificity of emotions, few have explored what effects the induction methods, themselves, have had in inducing the autonomic change. Autonomic specificity of induction methods might be a meaningful, and confounding, phenomenon in this literature. Based on this unknown variable, the current experiment was designed to see if methods for emotion elicitation could be meaningfully captured by these same pattern classification techniques. This was accomplished using three separate emotion-elicitation methods to elicit five separate emotions. A sample of 64 college-aged students watched film clips, read imagery scripts, and recalled personal memories for five discrete emotions. Using discriminant analysis, the evidence from the current study lent less support for autonomic specificity of emotion than past experiments, and lends some support for providing future exploration into autonomic change that is related to methods for induction. Potential confounds and task fatigue effects are discussed. / Ph. D.

Workers are challenged by the increasingly complex multitasking environments they experience. To interact effectively with these environments, they must avoid overload. When workers get overloaded (when their mental demands exceed the resource capacity) quality drops, performance degrades, and safety suffers. What is largely unknown, however, is whether these results translate to postural tasks. Postural stability exhibits an entirely different set of challenges: injury, the danger of slips and falls, and risks associated with aging workers or those who have mental or physical challenges. An assembly line worker, for example, must assume different postures, interact with the product in some way, and react to visual and auditory alarms. Mistakes could be dangerous. It is clearly important, then, to understand the interactive effects of mental and postural workload. The goal of this research was to quantify the effects of mental and postural demands on overall workload. To accomplish this, we implemented three studies that were designed to capture the synergistic effects of different task types on overall workload and compare different types of workload measures against each other to help further design research in the area. We designed a dual-task mental/postural protocol to test the differential effects of a series of cognitive demands found in dual-task postural studied. The results of the first study depict a clear picture: the addition of an auditory task to unstable seating decreases postural sway. Based solely on this result, it might be concluded that workload did not increase. Using the same protocol while measuring mental workload however, we found that workload did in fact increase both subjectively and objectively, even when similar postural benefit was found. Even as performance seemed to improve, the participant moved nearer to possible overload and performance decrement (a condition we did not induce in this research). Based on the differences found between the different measures, we believe the importance of measuring overall workload as well as individual task performance in cognitive/postural dual-task research is very high. / Ph. D.

Previous naturalistic driving studies have shown that visual and manual secondary tasks increase driving crash risk. With the increasing use of infotainment systems in vehicles, secondary tasks requiring cognitive executive demand may increase crash risk, especially for young and older drivers. Naturalistic driving data were examined to determine if secondary tasks with increasing cognitive executive demand would result in increasing crash risk. Data were extracted from the Second Strategic Highway Research Program Naturalistic Driving Study, where vehicles were instrumented to record driving behavior and crash/near-crash data. Cognitive executive and visual-manual tasks paired with a second cognitive executive task were compared to the cognitive executive and visual-manual tasks performed alone. Crash/near-crash odds ratios were computed by comparing each task condition to driving without presence of any secondary task. Dual cognitive executive tasks resulted in greater odds ratios than those for single cognitive executive tasks. The dual visual-manual tasks odds ratios did not increase from single task odds ratios. These effects were only found for young drivers. These findings help validate that cognitive executive secondary task load increases crash/near-crash risk, especially in dual task situations for young drivers. Future research should be conducted to minimize cognitive task load associated with vehicle infotainment systems using such technologies as voice commands. / Master of Science / Previous naturalistic driving studies have shown that visual and manual secondary tasks increase driving crash risk. With the increasing use of infotainment systems in vehicles, secondary tasks requiring cognitive demand may increase crash risk, especially for young and older drivers. Naturalistic driving study data were examined to determine if secondary tasks with increasing proposed cognitive demand would result in increasing crash/near-crash risk. Cognitive and visual-manual tasks paired with a second cognitive task were compared to the cognitive and visual-manual tasks performed alone. It was found that dual cognitive tasks resulted in greater crash/near-crash risk than the single cognitive executive tasks. The dual visual-manual tasks did not show greater crash/near crash risk than the visual-manual tasks performed alone. These effects were only found for young drivers. These findings help validate that cognitive secondary task load increases crash/near-crash risk, especially in dual task situations for young drivers. Future infotainment systems and drivers' education programs should be designed to minimize cognitive loads.

We consider a multi-task learning problem, where an agent is presented a number of N reinforcement learning tasks. To solve this problem, we are interested in studying the gradient approach, which iteratively updates an estimate of the optimal policy using the gradients of the value functions. The classic policy gradient method, however, may be expensive to implement in the multi-task settings as it requires access to the gradients of all the tasks at every iteration. To circumvent this issue, in this paper we propose to study an incremental policy gradient method, where the agent only uses the gradient of only one task at each iteration. Our main contribution is to provide theoretical results to characterize the performance of the proposed method. In particular, we show that incremental policy gradient methods converge to the optimal value of the multi-task reinforcement learning objectives at a sublinear rate O(1/√k), where k is the number of iterations. To illustrate its performance, we apply the proposed method to solve a simple multi-task variant of GridWorld problems, where an agent seeks to find an policy to navigate effectively in different environments. / Master of Science / First, we introduce a popular machine learning technique called Reinforcement Learning (RL), where an agent, such as a robot, uses a policy to choose an action, like moving forward, based on observations from sensors like cameras. The agent receives a reward that helps judge if the policy is good or bad. The objective of the agent is to find a policy that maximizes the cumulative reward it receives by repeating the above process. RL has many applications, including Cruise autonomous cars, Google industry automation, training ChatGPT language models, and Walmart inventory management. However, RL suffers from task sensitivity and requires a lot of training data. For example, if the task changes slightly, the agent needs to train the policy from the beginning. This motivates the technique called Multi-Task Reinforcement Learning (MTRL), where different tasks give different rewards and the agent maximizes the sum of cumulative rewards of all the tasks. We focus on the incremental setting where the agent can only access the tasks one by one randomly. In this case, we only need one agent and it is not required to know which task it is performing. We show that the incremental policy gradient methods we proposed converge to the optimal value of the MTRL objectives at a sublinear rate O(1/ √ k), where k is the number of iterations. To illustrate its performance, we apply the proposed method to solve a simple multi-task variant of GridWorld problems, where an agent seeks to find an policy to navigate effectively in different environments.

No description available.

Augmented reality task guidance systems provide assistance for procedural tasks, which require a sequence of physical actions, by rendering virtual guidance visuals within the real-world environment. An example of such a task would be to secure two wood parts together, which could display guidance visuals indicating the user to pick up a drill and drill each screw. Current AR task guidance systems are limited in that they require AR system experts for use, require CAD models of real-world objects, or only function for limited types of tasks or environments. We propose a general-purpose AR task guidance approach and proof-of-concept system to generate guidance for tasks defined by natural language. Our approach allows an operator to take pictures of relevant objects and write task instructions for an end user, which are used by the system to determine where to place guidance visuals. Then, an end user can receive and follow guidance even if objects change location or environment. Guidance includes reusable visuals that display generic actions, such as our system's 3D hand animations. Our approach utilizes current vision-language machine learning models for text and image semantic understanding and object localization. We built a proof-of-concept system using our approach and tested its accuracy and usability in a user study. We found that all operators were able to generate clear guidance for tasks in an office room, and end users were able to follow the guidance visuals to complete the expected action 85.7% of the time without knowledge of their tasks. Participants rated that our system was easy to use to generate guidance visuals they expected. / Master of Science / Augmented Reality (AR) task guidance systems provide assistance for tasks by placing virtual guidance visuals on top of the real world through displays. An example of such a task would be to secure two wood parts together, which could display guidance visuals indicating the user to pick up a drill and drill each screw. Current AR task guidance systems are limited in that they require AR system experts for use, require detailed models of real-world objects, or only function for limited types of tasks or environments. We propose a new task guidance approach and built a system to generate guidance for tasks defined by written instructions. Our approach allows an operator to take pictures of relevant objects and write task instructions for an end user, which are used by the system to determine where to place digital visuals. Then, an end user can receive and follow guidance even if objects change location or environment. Guidance includes visuals that display generic actions, such as our system's 3D hand animations that mimic human hand actions. Our approach utilizes AI models for text and image understanding and object detection. We built a proof-of-concept system using our approach and tested its accuracy and usability in a user study. We found that all operators were able to generate clear guidance for tasks in an office room, and end users were able to follow the guidance visuals to complete the expected action 85.7% of the time without knowledge of the tasks. Participants rated that our system made it easy to write instructions and take pictures to create guidance visuals.

The use of teams and work groups in organizations has become increasingly more popular in the last decade. Within each of these groups, a leadership role must be filled in order for the team to achieve its task. This study posited that the method by which the leader comes into this role may have a direct impact on the group's performance and its perceptions of the group's interpersonal processes and efficiency, satisfaction with the group, satisfaction with the group output, and support for the leader. In addition, the influence of leader assignment was expected to change based on the level of interdependence required by the task. One hundred forty-eight subjects were assigned to one of four conditions in a 2X2 design (appointed vs. elected leaders X high vs. low interdependence task), and were asked to fill out a questionnaire upon completion of their task. Results found that the higher level of interdependence was significantly related to more favorable ratings of Perceived Group Efficiency ( F =6.89, p <.05) and Satisfaction with Group Output ( F =7.69, p <.05). Possible limitations and future research opportunities are addressed. / Master of Science