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Nowadays, researchers have shown convolutional neural network (CNN) can achieve an improved performance in multi-object tracking (MOT) by performing detection and re-identification (ReID) simultaneously. Many models have been created to overcome challenges and bring the state-of-the-art performance to a new level. However, due to the fact the CNN models only utilize feature from a local region, the potential of the model has not been fully utilized. The long range dependencies in spatial domain are usually difficult for a network to capture. Hence, how to obtain such dependencies has become the new focus in MOT field. One approach is to adopt the self-attention mechanism named transformer. Since it was successfully transferred from natural language processing to computer vision, many recent works have implemented it to their trackers. With the introduce of global information, the trackers become more robust and stable. There are also traditional methods which are re-designed in the manner of CNN and achieve satisfying performance such as optical flow. It can generate a correlated relation between feature maps and also obtain non-local information. However, the introduces of these mechanism usually causes a significant surge in computational power and memory. They also requires huge amount of epochs to train thus the training time is largely increased. To solve this issue, we propose a new method to gather non-local information based on the existing self-attention methods, we named it dual attention with regional-representation, which significantly reduces the training time as well as the inference time, but only causes a small increase in computational memory and are able to run with a reasonable speed. Our experiments shows this module can help the ReID be more stable to improve the performance in different tasks. / Thesis / Master of Applied Science (MASc)

Software bugs prediction is one of the most active research areas in the software engineering community. The process of testing and debugging code proves to be costly during the software development life cycle. Software metrics measure the quality of source code to identify software bugs and vulnerabilities. Traceable code patterns are able to de- scribe code at a finer granularity level to measure quality. Micro patterns will be used in this research to mechanically describe java code at the class level. Machine learning has also been introduced for bug prediction to localize source code for testing and debugging. Deep Learning is a branch of Machine Learning that is relatively new. This research looks to improve the prediction of software bugs by utilizing micro patterns with deep learning techniques. Software bug prediction at a finer granularity level will enable developers to localize code to test and debug during the development process.

This research explores the barriers that have hindered women?s ability to acquire top administrative positions in higher education in the Deep South. Previous studies document the fact that while more women are attending college nationally, far fewer women attain upper level administrative positions at their universities than do men. Sexism and family/work conflicts are known hindrances in women?s ability to assume key leadership roles in higher education. This research examines women?s perceptions of such obstacles in achieving top administrative positions at public universities in Alabama, Georgia, Louisiana, Mississippi, and South Carolina. Women administrators and women who are full and associate professors at both traditionally white and historically black colleges and universities (HBCU) were surveyed on their attitudes and perceptions of barriers affecting the representation of women in administrative and upper administrative positions. This research indicates that women largely believe that men are the key decision makers at their universities. However, contrary to my hypotheses, for those women faculty and administrators surveyed who believe that there are no barriers for women in achieving administrative or upper administrative posts, many of them state they have no intention in seeking higher positions. My research findings also reveal that finances is the primary motivator for many women faculty and administrators in moving up the administrative ladder. Women faculty and administrators with financially dependent families and those who simply desire to make more money state that they would seek administrative and upper administrative positions. Further, those women faculty members and administrators who perceive their institution as having familyriendly policies and practices indicated that they are not inspired to achieve an administrative or upper administrative position based on that factor.

Across the Deep South: A Linked Story Collection focuses on the establishment and reestablishment of themes that reflect the mutability of characters over time, along with the equally mutable notion of identity found within the cultural context of the modern Southern United States. The stories follow the paradigm of Sherwood Anderson’s linked story collection Winesburg, Ohio in that character and geographical location combine over the course of multiple stories to recontextualize theme and character development through intertextual cohesiveness. Preceding the collection of stories is a critical introduction that considers the linked story collection as an independent form of fiction occupying a distinct space between the non-interrelated short story collection and the novel.

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