Indigenous Language Reclamation: The Learners Perspective

ABSTRACT This study explored the link between learning an Indigenous language and the meanings second language learners attach to their language recovery experiences. The study delves into the factors that motivate, enhance and serve as barriers to individual language revitalization efforts. With the goal of reasserting an Indigenous world view, the traditional teachings of the Ojibwe medicine wheel were combined with the lessons of the seven Grandfathers to provide a methodological basis for conducting ethical research with and for the benefit of First Nations people. Within the context of our relationships with self, community, spirit and environment, the pairing of Indigenous theory with the practical community experiences of Indigenous second language learners, demonstrates how Indigenous systems of thought and ontology lend themselves well to the critical understanding necessary to enhance the recovery our own endangered languages. These research findings indicate that there is a definite link between ancestral language reclamation and increased levels of self-esteem, a sense of grounded cultural identity and resilience, an overall sense of healing and the social responsibility that comes with receiving the gift of language. The barriers associated with learning an ancestral language intersect on multiple and often simultaneous levels making it difficult for the language learners to discover their origin.This research found that it was important for language learners to identify that they often carry a collective sense of shame associated with an internalized attachment to the modality of Indigeneity. Once the origin of this shame was acknowledged – as resulting from settler/assimilation logics, it was often possible for people to move forward in their language recovery journeys, while at the same time considering more broadly the structural barriers that make individual learning so difficult.

The Values of Community Curling: A Case Study

This qualitative case study research shows that within the realm of curling, the professionalization of the sport, at the national level, has limited to no effect on the core values of respect, belonging, and giving back that the grassroots level of curling identify as important. Through an interview process with twelve community level curlers, from four separate clubs within the Niagara region, data were collected and analyzed using traditional coding techniques. Utilizing institutional theory, the research shows a growing gap between the national level of curling and the grassroots level. Data also shows that value alterations, at the community level, are based on the changing Canadian environment in regards to legislation (smoking and drinking laws) and social behaviours (the busier Canadian lifestyle) rather than changes at the national level. These findings have a profound effect on how sports are administered in the Canadian sport system

L-Fuzzy Relations in Coq

Heyting categories, a variant of Dedekind categories, and Arrow categories provide a convenient framework for expressing and reasoning about fuzzy relations and programs based on those methods. In this thesis we present an implementation of Heyting and arrow categories suitable for reasoning and program execution using Coq, an interactive theorem prover based on Higher-Order Logic (HOL) with dependent types. This implementation can be used to specify and develop correct software based on L-fuzzy relations such as fuzzy controllers. We give an overview of lattices, L-fuzzy relations, category theory and dependent type theory before describing our implementation. In addition, we provide examples of program executions based on our framework.

Characterizing Dynamic Optimization Benchmarks for the Comparison of Multi-Modal Tracking Algorithms

Population-based metaheuristics, such as particle swarm optimization (PSO), have been employed to solve many real-world optimization problems. Although it is of- ten sufficient to find a single solution to these problems, there does exist those cases where identifying multiple, diverse solutions can be beneficial or even required. Some of these problems are further complicated by a change in their objective function over time. This type of optimization is referred to as dynamic, multi-modal optimization. Algorithms which exploit multiple optima in a search space are identified as niching algorithms. Although numerous dynamic, niching algorithms have been developed, their performance is often measured solely on their ability to find a single, global optimum. Furthermore, the comparisons often use synthetic benchmarks whose landscape characteristics are generally limited and unknown. This thesis provides a landscape analysis of the dynamic benchmark functions commonly developed for multi-modal optimization. The benchmark analysis results reveal that the mechanisms responsible for dynamism in the current dynamic bench- marks do not significantly affect landscape features, thus suggesting a lack of representation for problems whose landscape features vary over time. This analysis is used in a comparison of current niching algorithms to identify the effects that specific landscape features have on niching performance. Two performance metrics are proposed to measure both the scalability and accuracy of the niching algorithms. The algorithm comparison results demonstrate the algorithms best suited for a variety of dynamic environments. This comparison also examines each of the algorithms in terms of their niching behaviours and analyzing the range and trade-off between scalability and accuracy when tuning the algorithms respective parameters. These results contribute to the understanding of current niching techniques as well as the problem features that ultimately dictate their success.

An Abstract Algebraic Theory of L-Fuzzy Relations for Relational Databases

Classical relational databases lack proper ways to manage certain real-world situations including imprecise or uncertain data. Fuzzy databases overcome this limitation by allowing each entry in the table to be a fuzzy set where each element of the corresponding domain is assigned a membership degree from the real interval [0…1]. But this fuzzy mechanism becomes inappropriate in modelling scenarios where data might be incomparable. Therefore, we become interested in further generalization of fuzzy database into L-fuzzy database. In such a database, the characteristic function for a fuzzy set maps to an arbitrary complete Brouwerian lattice L. From the query language perspectives, the language of fuzzy database, FSQL extends the regular Structured Query Language (SQL) by adding fuzzy specific constructions. In addition to that, L-fuzzy query language LFSQL introduces appropriate linguistic operations to define and manipulate inexact data in an L-fuzzy database. This research mainly focuses on defining the semantics of LFSQL. However, it requires an abstract algebraic theory which can be used to prove all the properties of, and operations on, L-fuzzy relations. In our study, we show that the theory of arrow categories forms a suitable framework for that. Therefore, we define the semantics of LFSQL in the abstract notion of an arrow category. In addition, we implement the operations of L-fuzzy relations in Haskell and develop a parser that translates algebraic expressions into our implementation.

L-Fuzzy Structured Query Language

Lattice valued fuzziness is more general than crispness or fuzziness based on the unit interval. In this work, we present a query language for a lattice based fuzzy database. We define a Lattice Fuzzy Structured Query Language (LFSQL) taking its membership values from an arbitrary lattice L. LFSQL can handle, manage and represent crisp values, linear ordered membership degrees and also allows membership degrees from lattices with non-comparable values. This gives richer membership degrees, and hence makes LFSQL more flexible than FSQL or SQL. In order to handle vagueness or imprecise information, every entry into an L-fuzzy database is an L-fuzzy set instead of crisp values. All of this makes LFSQL an ideal query language to handle imprecise data where some factors are non-comparable. After defining the syntax of the language formally, we provide its semantics using L-fuzzy sets and relations. The semantics can be used in future work to investigate concepts such as functional dependencies. Last but not least, we present a parser for LFSQL implemented in Haskell.