Breast Ultrasound Images Segmentation Using Deep Neural Networks

Breast Ultrasound (US) imaging has emerged as an important diagnostic technique for detecting and characterizing breast tumors. Accurate segmentation of breast US images plays an essential role in enhancing the efficiency and precision of clinical assessments. This report explores the application of several well-known deep neural networks to the breast US image segmentation task. Specifically, we train and evaluate the following five models: SegNet, U Net, and DeepLab V3+ with three different bondnets (ResNet-18, ResNet-50, and Xception). The presented results are based on two labeled datasets. One is Breast US Images (BUSI) dataset, which was used for training, validation, and testing. The other is Breast US Lesions (BUL) dataset, which was used exclusively for testing. Data augmentation was applied to increase the number and diversity of the data samples by randomly varying the contrast, brightness, and gamma of US images. The performance of each model was evaluated based on Global Accuracy, Mean Accuracy, Mean Intersection-over-Union (IoU), Weighted IoU, Mean Boundary-F1 (BF) score, Average Dice score of Background, Average Dice score of Tumor, Mean Dice score of Tumor, and the model's cost. Overall, our results showed that Xception-based DeepLab V3+ and U-Net outperformed the other models under consideration when segmenting breast US images.Graduat

Computer Classification of News

Facing the growing demand for psychological services, if we want computers to provide psychological services to humans, the first step is to identify human emotions accurately. This research is about how to enable computers to accurately identify text content, analyze it, and make judgments. In this process, the torchtext.datasets.AG_NEWS dataset was selected. AG_NEWS has four large classes ("World", "Sports", "Business", "Sci/Tech"). TfidfVectorizer was used to judge the importance of each word in a sentence. Common words like articles frequently appear in every sentence, so they are ignored. The importance of other words for sentence classification is judged based on their frequency of appearance – the higher the frequency of a word, the less weight it carries. Support Vector Machines are then used to optimize accuracy. The research showed AI tools can predict human emotions based on the interpretation of the text. If AI tools can accurately predict human emotions, this builds a foundation for the machines to respond appropriately to people needing psychological services.Jamie Cassels Undergraduate Research Awards (JCURA)UndergraduateReviewe

Numerical investigation of effect of mechanical compression on the transport properties of fuel cell microporous layer using a pore-scale model

The microporous layer (MPL) plays an important role in water and thermal management of proton exchange membrane fuel cells (PEMFCs). An in-depth investigation of the mechanical compression effect on transport properties in the MPL can help optimize cell performance. In this work, the microstructure of the MPL is numerically reconstructed and the finite element method is applied to simulate mechanical behavior. Besides, the distribution of stress-strain, porosity, and pore size in the MPL under ten different levels of mechanical compression strains are studied. Lastly, the pore-scale model is employed to investigate the effective transport properties of the MPL as a function of compression strain. The analysis reveals that as the MPL strain increases from 0% to 40%, there is a 29% decrease in porosity, a 50% reduction in average pore diameter, a 60% decrease in effective gas diffusivity, a 100% increase in tortuosity, and an 80% increase in electrical and thermal conductivity. With the escalation of mechanical compression, both the magnitude and uniformity of stress-strain-displacement concurrently rise. Mechanical compression strains below 20% exhibit a lesser impact on transport properties. Beyond this threshold, exceeding the 20% compression strain point, mechanical stress assumes a critical role in influencing MPL transport properties.This work was supported by National Natural Science Foundation of China (grant numbers 52306270); the Guangdong Basic and Applied Basic Research Foundation (grant number 2022A1515110456); Donghai Laboratory Open-end Fund, Zhoushan, China (grant number DH-2022KF0305); the research innovation team construction plan of Wuhan City Polytechnic College (grant number 2023whcvcTD01).FacultyReviewe

Global Otaku: Labels and Japanese Pop-Culture in Translation

The idea of what an otaku is and how an otaku might act has changed gradually over the years. The phrase in it of itself has its own cultures and subcultures that intermingle and disassociate from one and other in varying ways. However, the consensus of the term from a Japanese context is its use to describe people with a strong interest in a particular object or subject, that also lack social skills. From a more western perspective, otaku means for a general interest in Japanese popular culture and media. This research will be looking to understand the history/origins of otaku, then comparing that to how it holds up today. Additionally, it’ll be looking into of the usage of devices such as the Gatebox, online communities, and other virtual mediums used for communication. This serves the purpose of looking into what it means for the “Global Otaku” as a sphere of communication, both inside and outside of Japan.Jamie Cassels Undergraduate Research Awards (JCURA)UndergraduateReviewe

Log Message Anomaly Detection using Positive and Unlabeled Learning

Log messages are widely used in cloud servers and software systems. Anomaly detection of log messages is important as millions of logs are generated each day. However, besides having a complex and unstructured form, log messages are large unlabeled datasets which makes classification very difficult. In this thesis, a log message anomaly detection technique is proposed which employs Positive and Unlabeled Learning (PU Learning) to detect anomalies. Aggregated reliable negative logs are selected using the Isolation Forest, PU Learning, and Random Forest algorithms. Then, anomaly detection is conducted using deep learning Long Short-Term Memory (LSTM) network. The proposed model is evaluated using the commonly employed Openstack, BGL, and Thunderbird datasets and the results obtained indicate that the proposed model performs better than several well-known approaches in the literature.Graduat

Associations between stress, affect, and physical activity in young adulthood: Stages of change as potential moderators

Current global estimates of physical activity suggest that less than 20% of adolescents are sufficiently physically active, and 28% of adults over 18 are not active enough to stay healthy despite the known physical and mental health benefits. Given the notable short- and long-term benefits of physical activity, paired with insufficient engagement rates, young adulthood (ages 19-25) is a critical time to build and support continued physical activity engagement across the lifespan. Research has identified increased stress and negative mood to impair physical activity efforts, but the associations between stress and affect and physical activity at the daily levels remained largely unexplored. In addition, potential moderating factors of these associations currently lack research. This study aimed to understand the associations between daily stress, positive and negative affect, and physical activity, as well as explore the six Stages of Change (SoC) as potential moderating factors. Undergraduates (N = 74; Mage = 20.88, SD = 2.53) responded to surveys administered through a smartphone app for 14 days and wore a Fitbit Charge 2 to gather physical activity data (i.e., daily steps). Multi-level models showed no within-person associations between stress, positive and negative affect, and physical activity. However, two significant interactions were observed: (1) contemplation significantly moderated the association between positive affect and physical activity, and (2) action significantly moderated the association between negative affect and action. Overall, results concerning the moderating impact of SoC were mixed; yet provide directions for future research. Results can provide new insight for strategies that focus on strengthening personal intentions and promoting individual motivations to engage in health behaviours such as physical activity.Graduat

Harnessing image-based deep learning for advanced malware classification

This thesis explores the application of image-based deep learning models for malware classification, leveraging a subset of the extensive MalNet-Image dataset, which includes around 87,000 binary images from a base of 1.2 million binary images based on Android APK files. The core contribution of this work lies in the innovative use of multiple components that, as far as we know, have not been used before to tackle the malware classification problem. Harnessing the power of deep neural networks (DNNs), which have demonstrated exceptional capabilities in various classification tasks, we aim to enhance the accuracy and efficiency of malware detection. These include Feature Pyramid Networks (FPN) to handle the file size scale issue when converting to images and the application of data augmentation techniques like MIXUP and TrivialAugment. We employ transfer learning with pre-trained models on ImageNet and optimize them using the AdamW Schedule-Free optimizer. Our experimental results show that the integration of these techniques achieves remarkable improvement in classification accuracy, with our best model achieving an F1 score of 0.6927 compared to 0.65 reported on the provided split for MalNet-Tiny. This could be considered a step forward in the field of malware classification using image-based deep learning models.Graduate2025-08-2

Corrosive comparisons and the memory politics of "saming": Threat and opportunity in the age of apology

This article contributes to the interdisciplinary fields of memory and historical justice studies by analyzing one, particularly troublesome kind of competitive comparison that sometimes happens in memory politics in the so-called age of apology. The article calls this kind of competitive comparison, “saming”. Saming involves the attempt, via far-fetched or otherwise wrongheaded comparison, to exploit the recognition of some well-known case of historical injustice. Further, saming involves pursuing this comparison in ways that both trivialize the original injustice and undermine the framework from which the recognition of that injustice derives. The article develops its arguments and analysis by studying Budapest’s House of Terror museum and two Canadian redress campaigns, which sought historical recognition for the wartime internments of persons of Italian and Ukrainian ancestry, respectively. Saming is a recurrent problem, ubiquitous and probably inevitable in memory politics because the recognition of historical injustice brings with it unavoidable and indeed often valuable incentives to comparison. Thus, the overall aim of this article is to analyze the threat of saming in order to better defend the cause of comparison in introspective collective remembrance.This research was funded by the Social Sciences and Humanities Research Council of Canada, grant number 895-2022-1000.FacultyReviewe

Shaping the future of management education with deliberative pedagogy

My research was focused on Deliberative Pedagogy, Data Generation Processes, and Management Constructs, with intersections among these areas. Initially, I explored and summarised articles that experiment with deliberative pedagogy. In my notes, I included a list of their procedures, data generation methods, and learning outcomes. I then detailed these data generation processes, noting the measurement tools used, such as surveys, interviews, and reflections. Our study is planned to be taught in a business class and hopefully published in a top management journal. Therefore, I investigated key management constructs discussed in four prominent journals: Academy of Management Learning and Education, Management Learning, Journal of Management Education, and Journal of Business Ethics. This involved analysing the frequency and discussion around these constructs in the journals, their citation counts, measurement approaches, and their links to deliberative pedagogy. Finally, I identified and recommended five main constructs worth including in our study, each with relevant sub-constructs: (1)Leadership (with focus on humanistic approaches and self-identity),(2) Ecological Awareness, (3)Business Ethics (highlighting empathy and ethical decision-making), (4)Interpersonal Efficacy (general teamwork, listening, and inclusion), and (5)Geopolitical Awareness. These constructs were presented to my advisor as potential things to test for improvements in our study.Valerie Kuehne Undergraduate Research Awards (VKURA)UndergraduateReviewe

Animals as Property, Quasi-Property or Quasi-Person

The field of animal law has been structured for the last twenty-five years around two key paradigms: (i) property v. persons and (ii) rights v. welfare. In this talk, Professor Fernández explains why both of these binaries are unhelpful traps for the field and why she thinks we need to move towards a more flexible legal status for nonhuman animals, which she terms quasi-property/quasi-personhood. In the quasi-hood approach to the legal status of nonhuman animals, nonhuman animals are more than mere property but they are also persons of a sort, legal persons, because they have legal rights, and only legal persons have legal rights.UVic Graduate Student Law & Society Research GroupFacultyUnreviewe