Clustering procedures suitable for the analysis of very high-dimensional data are needed for many modern data sets. One model-based clustering approach called high-dimensional data clustering (HDDC) uses a family of Gaussian mixture models to model the sub-populations of the observed data, i.e., to perform cluster analysis. The HDDC approach is based on the idea that high-dimensional data usually exists in lower-dimensional subspaces; as such, the dimension of each subspace, called the intrinsic dimension, can be estimated for each sub-population of the observed data. As a result, each of these Gaussian mixture models can be fitted using only a fraction of the total number of model parameters. This family of models has gained attention due to its superior classification performance compared to other families of mixture models; however, it still suffers from the usual limitations of Gaussian mixture model-based approaches. Herein, a robust analogue of the HDDC approach is proposed. This approach, which extends the HDDC procedure to include the mulitvariate-t distribution, encompasses 28 models that rectify one of the major shortcomings of the HDDC procedure. Our tHDDC procedure is fitted to both simulated and real data sets and is compared to the HDDC procedure using an image reconstruction problem that arose from satellite imagery of Mars' surface. / Thesis / Master of Science (MSc)

yes / We are pleased to welcome you to the 17th edition of the Advances in Model-Based Testing Workshop (A-MOST 2021), collocated with the IEEE International Conference on Software Testing, Verification and Validation (ICST 2021).

Block-based programming languages were originally designed for educational purposes. Due to their low requirements for a user's programming capability, such languages have great potential to serve both introductory programmers in educational settings as well as domain experts as a data processing tool. However, the current design of block-based languages fails to address critical factors for these two audiences: 1) domain experts do not have the ability to perform crucial steps: import data sources, perform efficient data processing, and visualize results; 2) the focus of online assignments towards introductory programmers on entertainment (e.g. games, animation) fails to convince students that computer science is important, relevant, and related to their day-to-day experiences. In this thesis, we present the design and implementation of DataSnap, which is a block-based programming language extended from Snap!. Our work focuses on enhancing the state of the art in block-based programming languages for our two target audiences: domain experts and introductory programmers. Specifically, in this thesis we: 1) provide easy-to-use interfaces for big data import, processing, and visualization methods for domain experts; 2) integrate relevant social media, geographic, and business-related data sets into online educational platforms for introductory programmers and enable teachers to develop their own real-time and big-data access blocks; and 3) present DataSnap in the Open edX online courseware platform along with customized problem definition and a dynamic analysis grading system. Stemming from our research contributions, our work encourages the further development and utilization of block-based languages towards a broader audience range. / Master of Science

Interactive visual analysis has been a key component of gaining insights in information visualization area. However, the amount of data has increased exponentially in the past few years. Existing information visualization techniques lack scalability to deal with big data, such as graphs with millions of nodes, or millions of multidimensional data records. Recently, the remarkable development of Graphics Processing Unit (GPU) makes GPU useful for general-purpose computation. Researchers have proposed GPU based solutions for visualizing big data in graphics and scientific visualization areas. However, GPU based big data solutions in information visualization area are not well investigated. In this thesis, I concentrate on the visualization of big data in information visualization area. More specifically, I focus on visual exploration of large graphs and multidimensional datasets based on the GPU technology. My work demonstrates that GPU based methods are useful for sensemaking of big data in information visualization area. / Master of Science

Yes / Summary of: Alberti S, Motta P, Ferri P, Bonetti L. The effectiveness of team-based learning in nursing education: A systematic review. Nurse Educ Today. 2021 Feb;97:104721. doi: 10.1016/j.nedt.2020.104721. Epub 2020 Dec 11.

People perceive social conflict and conflict issues in different ways. My research is directed at understanding how leaders from some of Winnipeg’s Community-Based Organizations (CBOs; faith and ethno-cultural NGOs) perceive and experience social conflict and to explore their contributions toward peace-building and conflict transformation. Historically, Winnipeg has been home to a plethora of faith-based, ethno-cultural community organizations, and NGOs whose mission is to provide crucial basic and spiritual needs to people. Their contribution to the nurturing of both the spiritual and social needs of their communities is also remarkable. This qualitative research is based on semi-structured interviews and participant observation as research instruments to observe social events related to conflict and peace-building. Critical ethnographic and grounded theory approaches inform the methodology while drawing necessary inferences from relevant quantitative data. From this research, several key findings become evident: 1) CBO leaders have a high level of personal motivation and employ a wide range of tools, such as the social capital of their organizations, to intervene in social conflict issues for the purpose of peace-building; 2) of the three types of CBO (faith, ethno-cultural and NGOs), the Faith-Based Organizations (FBOs) are the most networked and involved in transforming social conflicts and contributing toward peace-building in Winnipeg; 3) some respondents seemed to indicate that not all projects are contributing equally in addressing social conflict issues; 4) social conflict issues appear to be rooted in an unjust social structure and a number of socio-economic-political and cultural policies; 5) research participants cited five complex, interrelated conflict issues in Winnipeg; 6) a grounded theoretical concept (Perception-Expectation-Frustration) was generated to explain social conflict; and 7) these everyday leaders are using a plethora of strategies as everyday peace-builders who are engaged global citizens, and citizen diplomats to create oases of peace in a society where people are struggling with social inequality, discrimination, and poverty in order to address people's immediate needs, promote awareness, and influence policy. In sum, the meso level CBO leaders perceive social conflict holistically and some of their peace-building projects may be contributing substantially towards a long-term process of social conflict transformation in Winnipeg. / February 2017

The purpose of this dissertation is to examine the effects of activity-based costing (ABC) and activity-based management (ABM) on managerial and employee behavior.

Numerous research studies have shown that when teachers take children outdoors to learn, children show an increase in cognitive, physical, social, and emotional skills. Few researchers have focused, however, on teachers and their decision to use the outdoors as a way of teaching. The purpose of this qualitative study was to explore whether life experiences impact a teacher's choice to not use the outdoor environment. Ham and Shuman's model of environmental education commitment and Kaplan and Kaplan's environmental cognition theory served as the conceptual frameworks for this study. The research question was designed to explore the experiences and barriers of teachers and how these experiences and barriers affected a teacher's decision to use or not use an outdoor classroom when one was available. Data were obtained using individual interviews of a purposeful sample of seven elementary teachers from a large school district in the U.S. state of Georgia who were not using outdoor classrooms at the time of the study. Themes that emerged from data analysis were lack of time in tightly controlled class schedules, lack of administrator support, lack of staff development for teachers, weather, and lack of time to research and prepare lessons. Study findings have the potential to engender positive social change by increasing insight about the barriers teachers perceive to using the natural environment in instruction. With more knowledge about such barriers, administrators may able to encourage teachers to use the natural environment as an extension of the indoor classroom to increase academic achievement and lifelong behaviors in nature among students.

Knowledge representation and inference are the backbone of artificial intelligence, and logic programming is one of the most widely used knowledge representation tools. Logic programming with deduction/induction/abduction as the reasoning technique is serving numerous fields of artificial intelligence. In dynamic domains where there are constant changes in knowledge, updating the knowledge base is crucial to keep it stable. This thesis investigates the issues in updating the knowledge base. Two types of logic program based updates are considered, simple fact based updates where the knowledge base is updated by a simple fact, and rule based updates where the knowledge base is updated by a rule. A SLDNF based procedural approach is proposed to implement such updates. This thesis also investigates the issues involved in simple fact based and rule based abduction, and it is observed that updates are closely related to abduction. A SLDNF based procedural approach to perform simple fact/rule based updates and abduction is proposed as a result of this study / Master of Science (Hons)

Denna kvalitativa studie genomfördes för att undersöka vilken roll utvärdering av belöningssystem har i företag verksamma i Sverige. Studien utgår från teorin Evidence- Based Practice och dess underkategorier som specifikt behandlar belöningssystem. Dessa teorier applicerades på data inhämtad från intervjuer med personer ansvariga för belöningssystem på olika företag. I studien framkom, i likhet med de få tidigare studier som genomförts inom området, att utvärdering av belöningssystem inte har någon central roll hos de undersökta företagen. Anledningen till denna undanskymda roll anses dock inte vara en misstro mot behovet av det. Istället framkom att en starkt bidragande orsak till det restriktiva utförandet av utvärdering är att området anses komplext med stora problem relaterade till att identifiera lämpliga metoder för det praktiska utförandet.