identifier: Akbarzadeh, Mohammad  (2020) Enhancing Safety on Construction Sites by Detecting Personal Protective Equipment and Localizing Workers Using Computer Vision Techniques.  Masters thesis, Concordia University.

abstract:
The construction industry is among the world's most dangerous industries, with a high number of accidents and fatalities. Following safety guidelines and wearing the required Personal Protective Equipment (PPE) is an essential step in mitigating accidents. Safety managers and inspectors are responsible for making sure safety regulations are correctly followed. However, safety inspection is time-consuming, costly, and is done based on a random basis and for a short period.
In order to facilitate safety inspection, various research studies are done using different techniques and technologies. Detecting PPE using Computer Vision (CV) has gained a lot of interest in enhancing construction sites' safety. Nevertheless, detecting PPE on large construction sites and generating safety reports is still a big challenge. Additionally, real-world 2D localization of workers is critical to monitor workers’ safety based on their location. This research proposes an automated framework consists of three modules to enhance the safety of construction sites.
The first module of the framework is the PPE Detection (PPED) module, which detects and tracks the workers and their PPE on large construction sites based on the frame segmentation technique. The second module is the PPE Safety Report Generation (PPESRG), which uses PPED results to match workers in two overlapping views and generate technical and practical high-level safety reports while protecting workers’ privacy. Finally, the third module of the framework is a Single-camera Localization (SL) module that uses worker detection results from the PPED module and camera calibration parameters to locate workers on 2D real-world coordinate and monitor workers’ safety based on their location on the construction site.

The proposed framework is validated using real-world construction videos, and the experimental results of each module demonstrate the practicality and robustness of applying on real-world construction sites. Based on different test videos, the PPED module has achieved 99.04% precision, 91.61% recall, and 90.77% accuracy. Furthermore, the generated safety reports are validated by the safety managers of the project as being practical for safety monitoring on the construction sites. Finally, the proposed CL module is validated with an average error of the average 1.58 m for locating workers on the construction sites.
The main contributions of this research are: (1) proposing a nested network based on frame segmentation technique that improved the worker and PPE detection rate on large construction sites, (2) proposing a safety report generation method, which benefits from PPED results of two cameras to generate practical safety reports while protecting workers’ privacy and (3) single-camera based technique which is fast and easy to implement on large construction sites in order to locate workers. Future works will focus on accelerating the detection process, improving CV-based localization accuracy, and benefitting from other data sources to enhance generated safety reports (e.g., schedule, etc.).


identifier: Weisgarber, Brittany  (2020) Exploring the impact of mainstream and culturally specific programs for Indigenous women’s healing in a Quebec women’s provincial prison.  Masters thesis, Concordia University.

type: Thesis NonPeerReviewed

abstract:
Indigenous women are the fastest growing offender population in Canada. The largest increase in Indigenous women’s admissions to provincial/territorial custody between 2007 and 2018 occurred in Quebec, with a 219% increase (Statistics Canada, 2019). These statistics, coupled with a lack of localized (e.g., province-specific) knowledge regarding circumstances of Indigenous women, highlight an urgent need for identification and understanding of services needed to support Indigenous women in prison.

The purpose of this study was to explore Indigenous women’s perspectives on their participation in culturally specific and mainstream programs in a Quebec provincial women’s prison: Établissement de détention Leclerc de Laval. The study’s approach blended Indigenous and Western approaches to data collection and employed a relational ethics process for collaboratively building the study with women who participated. As a part of the study researchers and Indigenous women participated in three-day medicine bag workshops facilitated by Indigenous Elders and healers. Arts-based methods including storytelling, creation of medicine bags, sharing circles, and Sketchnotes (arts-based note taking; Rhodes, 2013) were used to collect data during workshops. The women also took part in optional interviews following workshops to share individualized insights regarding the impacts of the workshop and other correctional programs on their lives.

This thesis presents findings from five workshops and 31 interviews regarding the role of culturally specific and mainstream prison programs in supporting Indigenous women’s healing from the women’s perspectives. It further presents assets and limitations of available programs from the women’s perspectives. Program and policy implications of the findings are also presented.


identifier: Rezaei, Mahsa  (2020) Carpal Bone Analysis using Geometric and Deep Learning Models.  Masters thesis, Concordia University.

abstract:
The recent trend for analyzing 3D shapes in medical application has arisen new challenges for
a vast amount of research activities. Quantitative shape comparison is a fundamental problem
in computer vision, geometry processing and medical imaging. This thesis is motivated by the
availability of carpal bone shape dataset to develop efficient techniques for diagnosis of a variety
of wrist diseases and examine human skeletal.
This study is conducted in two sections. First, we propose a spectral graph wavelet approach
for shape analysis of carpal bones of the human wrist. More precisely, we employ spectral graph
wavelets to represent the cortical surface of a carpal bone via the spectral geometric analysis of the
Laplace-Beltrami operator in the discrete domain. We then propose global spectral graph wavelet
(GSGW) descriptor that is isometric invariant, efficient to compute and combines the advantages of
both low-pass and band-pass filters. Subsequently, we perform experiments on shapes of the carpal
bones of ten women and ten men from a publicly-available database of wrist bones. Using one-way
multivariate analysis of variance (MANOVA) and permutation testing, we show through extensive
experiments that the proposed GSGW framework gives a much better performance compared to
the global point signature (GPS) embedding approach for comparing shapes of the carpal bones
across populations.
In the second section, we evaluate bone age to assess children’s biological maturity and to diagnose
any growth disorders in children. Manual bone age assessment (BAA) methods are timeconsuming
and prone to observer variability by even expert radiologists. These drawbacks motivate
us for proposing an accurate computerized BAA method based on human wrist bones X-ray images.
We also investigate automated BAA methods using state-of-the-art deep learning models that
estimate the bone age more accurate than the manual methods by eliminating human observation
variations. The presented approaches provide faster assessment process and cost reduction in the
hospitals/clinics. The accuracy of our experiments is evaluated using mean absolute error (MAE),
and the results demonstrate that exploiting InceptionResNet-V2 model in our architecture achieves
higher performance compared to the other used pre-trained models.


identifier: Badawi, Ghislaine  (2020) The role of social context and anticipatory anxiety in social anxiety risk for pre-drinking.  PhD thesis, Concordia University.

abstract: Pre-drinking (drinking before going out) is prevalent among undergraduate students. Cognitive models of social anxiety and tension reduction theory propose that those high in social anxiety are at increased risk for pre-drinking to reduce anticipatory anxiety prior to social events. Pre-drinking occurs in various social contexts that differ in how anxiety-provoking they are for these individuals. Accordingly, those high in social anxiety may be at increased risk for pre-drinking to reduce anticipatory anxiety in specific contexts. The goal of this dissertation was to incorporate pre-drinking contexts and anticipatory anxiety into theoretically-based models of social anxiety and pre-drinking. In a daily diary study (Study 1), we tested the combined effect of in the moment social context and anxious mood on social anxiety-related pre-drinking. We hypothesized that elevated social anxiety would predict increased likelihood of pre-drinking and pre-drinking quantity when anxious mood was high in less familiar contexts. Partially supporting hypotheses, those high in social anxiety were more likely to be in less familiar contexts during pre-drinking when anxious mood was high, but did not pre-drink more heavily. Although not previously hypothesized, elevated social anxiety protected against pre-drinking when anxious mood was high in group contexts. In a laboratory experiment (Study 2), we examined the effect of social context on social anxiety-related increase in anticipatory anxiety and urge to drink prior to a social event. To provide the opportunity to pre-drink, participants completed a taste rating task in one of three social contexts and in an alcohol or no alcohol condition, following anticipatory anxious mood manipulation of an upcoming social interaction. We hypothesized that, compared to the no alcohol condition, those high in social anxiety would report increased anticipatory anxiety and urge to pre-drink when alone or with a stranger. Results indicated that those high in social anxiety reported increased anticipatory anxiety prior to the social interaction. Participants reported increased urge to pre-drink when alone and when with a stranger. Exploratory analyses suggested that social context may moderate social anxiety related increase in anticipatory anxiety and urge to pre-drink. The findings shed light on risk factors that could be targeted in clinical interventions aimed at reducing social anxiety-motivated problem drinking.


identifier: Bae, Jun Hyung  (2020) Identifying and Isolating a Flat Band in 2D Systems.  Masters thesis, Concordia University.

abstract: Flat band systems are gaining popularity due to special properties. For instance, the strong correlation of electrons in flat bands leads to realization of unconventional superconductivity. Typically, such bands are only approximately flat and are engineered by fine tuning Vanderwaal’s structures. On the other hand, systems with perfectly flat bands provide a ground for studying exotic quasi-particles such as composite fermions in the fractional quantum Hall state. These flat bands, however, are induced by an external field. Here we explore other systems that host perfectly flat bands, namely the Kagome lattice and the Lieb lattice. One issue with these lattices, however, is that their flat bands are degenerate with other bands. This thesis will explore means to lift the degeneracy while preserving the flatness of the band. It will also demonstrate that the flatness is robust under certain modifications to the lattice and that, unlike suggested by past studies, breaking time-reversal symmetry is not sufficient to isolate the flat band. Instead, we will show that modulating the flux arising from a Chern-Simons type field gaps out the band without disturbing its flatness.


identifier: Allard, Marc Alexandre  (2020) Performance and Wake Analysis of a Darrieus Wind Turbine on the Roof of a Building using CFD.  Masters thesis, Concordia University.

abstract: In order to reduce greenhouse gas emissions and counter climate change, efforts have been made towards the development of technologies in the field of renewable energies. Wind turbines emerged has a result of these efforts. A significant amount of research has been made to increase their power producing capabilities. The concept of positioning micro-scale wind turbines on the roof of buildings is currently being studied due to the possible benefits of onsite power generation. The research detailed in this thesis concentrates on the performance and wake analysis of a Darrieus wind turbine, with a Troposkien shape, located above the roof of a cubic building at two different positions and operating under different wind flow conditions. The results presented are obtained from 3D unsteady Computational Fluid Dynamics (CFD) simulations and the applied methodology is validated by comparing coefficient of power (Cp) data from validation cases with a Cp – λ curve acquired experimentally by Sheldahl. The first position considered is above the center upstream edge of the building, whereas the second one is located above one of the upstream building roof’s corners at a lower height. An atmospheric boundary layer is enforced at the inlet of the domain with a selected desired velocity pointing at the center of the rotor. Cp values from the roof-mounted simulations are computed using various tip speed ratios and three different wind directions. Furthermore, the rotor’s wakes in each scenario are measured and their behaviors are discussed. It is found that at a tip speed ratio of 5, the turbine’s Cp can be increased from 0.318 to 0.549 by positioning it above the building’s corner. Analysis of the rotor wake’s structure at this location also revealed that because the wake mixes with the surrounding accelerated flow, it is shorter than when operating in a freestream.


identifier: Delir Jafarzadehazar, Siamak  (2020) Shell Analysis of Steel Frames Considering Low-cycle Fatigue within the Continuum-Damage-Plasticity Framework.  Masters thesis, Concordia University.

abstract: In the event of an earthquake, steel-frames are subjected to cyclic loads.  Within the earthquake duration, the accumulated strain in ductile members may cause micro-scale fracture of steel material due to low-cycle fatigue, which manifests itself as early yielding and stiffness degradation at macro-scale stress-strain relations. Steel moment resisting frame members are made of thin-walled cross-sections, for which local or lateral-torsional buckling failure mode is considered in design. In order to capture local buckling, material yielding and lateral-torsional buckling modes sophisticated modelling approaches need to be adopted. When shell elements are used, multi-axial material models are needed and for this purpose Continuum Damage Plasticity (CDP) framework is used to build an inelastic multi-axial stress-strain relationship. The CDP has the capability of representing both the permanent deformations due to the plastic component and the degradation of elastic moduli due to the damage component. Early yielding and stiffness degradation of the material can be captured by adopting hardening/softening and damage accumulation criteria specific to the low-cycle behaviour of metals. In this research, an in-house computer program was developed. The program has the capabilities to impose Multi-Pont Constraints (MPC) which makes it flexible to generate the meshes for columns, beams and panel zones separately and then to bring them together. After validation of the program by using results of studies from literature, the possibilities of modelling different failure modes were investigated. MPCs were also used to simulate the beam behaviour as a special case of the shell model in which lateral-torsional buckling modes of thin-walled cross-sections of steel frames were captured under cyclic loads. It has been shown that considerations of local- and lateral-buckling modes as well as the low-cycle fatigue effect in the material cause significant differences in predicting the behaviour of steel frames under cyclic loads.


identifier: Karimi Nemch, Amirreza  (2020) Modeling Diffusion in the Gasification Reaction with Carbon Dioxide: Comparison of Diffusion Models.  Masters thesis, Concordia University.

abstract:
As one of the most effective ways to produce energy from a solid carbon, gasification becomes more popular among the industries. The feedstock of gasification passes through multiple steps and reactions to produce syngas, which is a mixture of CO and H2. However, diffusion limitations play an important role in the kinetics of gasification reaction and should be determined. A thermogravimetric analyzer (TGA) is used to investigate the gasification kinetics by studying the mass loss of the sample over time. The influence of mass transfer limitations is typically evaluated using an effectiveness factor, which is a function of the Thiele modulus.
In this work, a mathematical model is developed in MATLAB to investigate the mass transfer limitations in the CO2 gasification of petroleum coke and activated carbon using different diffusion theories and various scenarios. Unlike previous studies, various multicomponent and binary diffusion flux theories are compared in this work. Starting with Fick’s law of diffusion, which is the simplest diffusion theory for binary mixtures to the most recent ones such as the dusty-gas model (DGM), and the cylindrical pore interpolation model (CPIM).
Simulation results show that using Fick’s law (binary case) may underestimate the diffusion limitations in the bed and the gas part. Equally important are the assumptions made about the fate of minor elements in the carbonaceous material, such as oxygen or hydrogen. Therefore, a multicomponent flux theory is required to model the diffusion. The dusty-gas model provides a better estimation of the experimental rates compared to the other theories (CPIM and Fick’s law). Moreover, the diffusion limitations are more significant for the more reactive component, activated carbon, and cannot be eliminated. For petcoke, external diffusion is the mass transfer limiting step in a closed-bottom crucible. For activated carbon, the effects of external and inter-particle diffusion on the overall rate are almost the same order of magnitude. Eventually, the model is verified with the experimental results of Malekshahian et al. (2014). The proposed model shows a good consistency with the experimental results in all initial masses. However, the intrinsic rate constant should be modified with a kinetic model accounting for CO inhibition to enhance the match with the experimental results.


identifier: Mirzai Tavana, Sina  (2020) Thermal Spray Coating of Carbon Reinforcement Composites for Wear and Erosion Resistance Applications.  Masters thesis, Concordia University.

abstract: Polymeric composite materials have been used to manufacture both jet engine fan blades and wind turbine blades. The main considerations in the blades design are efficiency, weight, and durability. The blades must be capable of withstanding solid particles impact with no damage. Polymeric composite does not have a good erosion resistance.  In this thesis, application of thermal spray coatings on polymeric composite materials has been investigated to improve their erosion resistance.  Three different coating materials namely, tungsten carbide-cobalt, martensitic chromium stainless steel, and alumina-titania are chosen because of their excellent abrasion and erosion resistant, good mechanical properties, and relatively low cost. Atmospheric plasma spray is used to deposit the coating on the composite. Composite panels are manufactured using carbon fiber reinforced polymer CFRP using hand layup and autoclave curing. During the layup, a stainless steel mesh is placed on the top of the panel. This metal mesh will protect the composite during coating deposition and serve as an anchor for keeping the coating in place. Different plasma spray processing conditions are tried to have a good coating on the panel. Flatwise tensile tests are performed to measure the adhesion between the composite substrate and coating. It is found that using stainless steel metal mesh makes uniform, high quality coatings with significant adhesion property between composite substrate and coat layer. The solid erosion testing was carried out using air-jet erosion testing (ASTM G76) by hard, angular alumina particles at 30˚, 60˚ and 90˚ impact angle. Different impact angles were measured to demonstrate the erosion regime (ductile or brittle) of each material. In order to determine mechanical properties and microstructure effect, roughness and the hardness of each material have been studied. It is shown that the proposed method of manufacturing can improve significantly the erosion resistance of the polymeric composites.


identifier: Ahmadzadeh, Mohammad Sadegh  (2020) A Study on Entropy-Based Variational Learning for Mixture Models.  Masters thesis, Concordia University.

abstract:
Nowadays, we observe a rapid growth of complex data in all formats due to the technological development. Thanks to the field of machine learning, we can automatically analyze and infer useful information from these data. In particular, data clustering is regarded as one of the most famous data analysis tools aiming at grouping data with similar patterns into the same cluster. Among existing clustering techniques, finite mixture models have shown great flexibility in data modeling. Mixture models are a common unsupervised learning technique that have been widely used to statistically approximate and analyse heterogenous data. The goal of using mixture models is to fit the data into an appropriate distribution. A crucial point is to estimate the prefect parameters of the distribution and the suitable number of clusters in the data. To do so, an entropy-based variational learning algorithm is proposed for the model selection (i.e. determination of the optimal number of components). We investigate if a given component is genuinely distributed according to a mixture model to select the optimal number of components that better suits our data.
   In our work, we have used the variational inference framework that overcomes the over-fitting problem of maximum likelihood approaches and at the same time convergence is guaranteed. In addition, it decreases the computational complexity of purely Bayesian approaches. In recent researches the main concern when deploying mixture models has been the choice of distributions. The effectiveness of Dirichlet family of distributions has been proved in recent studies especially for non-Gaussian data.
   In this thesis, an effective mixture model-based approach for clustering and modeling purposes has been proposed. Our contribution is the application of an entropy-based variational inference algorithm to learn the mixture models, namely, generalized inverted Dirichlet and inverted Beta-Liouville mixture models. The performance of the proposed model is evaluated on multiple real-world applications such as human activity recognition, images, texture and breast cancer datasets, where in each case we compare our results with popular and similar models.


identifier: Ndejuru, Lisa  (2020) Oral History and Performance in the Aftermath of Organized Violence: An Epistemological Contribution.  PhD thesis, Concordia University.

abstract:
Oral History and Performance in the Aftermath of Organized Violence:
An Epistemological Contribution.
Lisa Ndejuru, Ph.D
Concordia University, 2020

Can transdisciplinary, relational research-creation strategies open pathways to wellness,
emancipation, and finding one’s voice in a post-colonial context of genocide, war, organized
violence, and exile?

What are some affordances of performative inquiry, writing as inquiry, and other arts-based pedagogies and practices when applied to oral histories, memory work and sense-making?

Can community dialogue, creative storytelling, deep listening help move toward healing in the aftermath of organized violence and traumatic loss, and exile?

Can improvisational playback theatre with difficult stories appease the silences, and help defeat intergenerational transmission of the traumas of persecution and genocide and war.

As a child and grandchild of survivors of early anti-Tutsi injustices in Rwanda, as a wife and mother, I seek non-professionalized and non-medicalized solutions––accessible metaphors, tools and techniques––for use within my own afrodiasporic community setting, and beyond, as a new generation works through questions of memory, identity, change and transformation.

My learnings emerge out of a very personal perspective, reflecting more than 20 years of experience as an activist and organizer within the Rwandan diaspora in Canada, my work as a licensed mental health professional focused on the wellness of racialized minorities, my creative collaborations as a community artist, and the community-based research I have undertaken as a volunteer co-applicant of the 7-year SSHRC-funded CURA project Life Stories of Montrealers displaced by genocide, war and other human rights abuses, based at the Centre for Oral History and Digital Storytelling at Concordia University.


title:
Oral History and Performance in the Aftermath of Organized Violence:
An Epistemological Contribution


identifier: Shoumarova, Lina  (2020) Affective Print Literacies in the Context of (Im)Migration.  Masters thesis, Concordia University.

abstract: The guiding argument of this study is that (im)migrants arrive in their host countries with diverse cultural traditions, knowledge and educational experiences and that these constitute strengths, not deficiencies. These strengths and resources can be viewed as a form of transcultural wealth that could be brought to light in both out-of-school and classroom settings, as well as on the community level, as part of the (im)migrants’ process of acculturation and integration into their new places of settlement. Furthermore, this study challenges the dominant instrumentalist rhetoric around which adult education programs for (im)migrants in Canada are often structured and which subsumes learning and literacy to market and employment exigencies—a reflection of our current historical moment of advanced neoliberal globalization. Literacy and education theorists have long called for the implementation in classrooms and on a policy level of pedagogical and assessment models, as well as a general disposition towards (im)migrant learners, based not on the language and literacy skills that they lack, but on those that they possess and which form their linguistic, cultural and literacy repertoires, in addition to being deeply connected to their very sense of self. By bringing into the discussion the affective dimension of literacy and learning, this thesis aims to counter the above-mentioned instrumentalist rationality as limited and to emphasize the importance of promoting cross-cultural communication and cross-cultural competencies in adult education. Ultimately, this project is situated within the greater challenge of us learning to live together as humanity, learning to better communicate with each other and accept our differences.


identifier: Farsi, Behnam ORCID: https://orcid.org/0000-0002-7287-2989 <https://orcid.org/0000-0002-7287-2989>  (2020) On Short-Term Load Forecasting Using Machine Learning Techniques.  Masters thesis, Concordia University.

type: Thesis PeerReviewed

abstract:
Since electricity plays a crucial role in industrial infrastructures of countries, power companies are trying to monitor and control infrastructures to improve energy management, scheduling and develop efficiency plans. Smart Grids are an example of critical infrastructure which can lead to huge advantages such as providing higher resilience and reducing maintenance cost. Due to the nonlinear nature of electric load data there are high levels of uncertainties in predicting future load. Accurate forecasting is a critical task for stable and efficient energy supply, where load and supply are matched. However, this non-linear nature of loads presents significant challenges for forecasting. Many studies have been carried out on different algorithms for electricity load forecasting including; Deep Neural Networks, Regression-based methods, ARIMA and seasonal ARIMA (SARIMA) which among the most popular ones.

This thesis discusses various algorithms analyze their performance for short-term load forecasting. In addition, a new hybrid deep learning model which combines long short-term memory (LSTM) and a convolutional neural network (CNN) has been proposed to carry out load forecasting without using any exogenous variables. The difference between our proposed model and previously hybrid CNN-LSTM models is that in those models, CNN is usually used to extract features while our proposed model focuses on the existing connection between LSTM and CNN. This methodology helps to increase the model's accuracy since the trend analysis and feature extraction process are accomplished, respectively, and they have no effect on each other during these processes. Two real-world data sets, namely "hourly load consumption of Malaysia" as well as "daily power electric consumption of Germany", are used to test and compare the presented models. To evaluate the performance of the tested models, root mean squared error (RMSE), mean absolute percentage error (MAPE) and R-squared were used. The results show that deep neural networks models are good candidates for being used as short-term prediction tools. Moreover, the proposed model improved the accuracy from 83.17\% for LSTM to 91.18\% for the German data. Likewise, the proposed model's accuracy in Malaysian case is 98.23\% which is an excellent result in load forecasting. In total, this thesis is divided into two parts, first part tries to find the best technique for short-term load forecasting, and then in second part the performance of the best technique is discussed. Since the proposed model has the best performance in the first part, this model is challenged to predict the load data of next day, next two days and next 10 days of Malaysian data set as well as next 7 days, next 10 days and next 30 days of German data set. The results show that the proposed model also has performed well where the accuracy of 10 days ahead of Malaysian data is 94.16\% and 30 days ahead of German data is 82.19\%. Since both German and Malaysian data sets are highly aggregated data, a data set from a research building in France is used to challenge the proposed model's performance. The average accuracy from the French experiment is almost 77\% which is reasonable for such a complex data without using any auxiliary variables. However, as Malaysian data and French data includes hourly weather data, the performance of the model after adding weather is evaluated to compare them before using weather data. Results show that weather data can have a positive influence on the model.

These results show the strength of the proposed model and how much it is stable in front of some challenging tasks such as forecasting in different time horizons using two different data sets and working with complex data.


identifier: Etheridge, Julie  (2020) Exploring How Teachers Apply Art Museum Professional Development to Their Practices.  PhD thesis, Concordia University.

abstract:
This doctoral dissertation investigated the art museum summer teacher workshop model of professional development with the intention to better understand how in-service generalist teachers and art specialist teachers apply their experiences and learning once they have returned to their classrooms. The usefulness of professional development programs experienced at the district level has often been questioned due to a lack of connection to actual classroom practice (Guskey, 2009). In contrast, the art museum summer institute model of professional development is considered more successful as it offers rich learning experiences and can provide more resources than school district-supported workshops (Charland, 2008). While some studies have investigated teachers learning art museum pedagogy (Hausman, 1966; Hsieh, 2008; Sandell and Zimmerman, 2017), they are all from the perspective of the museum educator. This study is positioned from the perspective of in-service generalist and art teachers and their framing of the curricular and pedagogical experience in museum summer institutes and their own classrooms. To investigate the phenomenon, I designed a comparative case study in which I acted as a participant-observer in two professional development art museum summer institute programs in 2018 and recruited a total of eight in-service teachers, whose use of the curricular materials in their classroom practices I followed over the 2018–2019 academic year. Concurrently I conducted an autoethnographic study of my own use of the curricular materials in my grade 6 classroom. To frame this study, I applied curriculum theorist Dwayne Huebner’s (1968/2008) approach to language to understand curricula, which also provided the interpretive framework to analyze the data along with his Five Value System (1966/2008). Participating generalist and art specialist educators in this study had distinct experiences at the summer institutes; the two groups approached the art museum curricula with different objectives and viewpoints. I found that the art museum was perceived as a safe space for art specialists; the generalist teachers readily incorporated works of art into their lessons yet avoided discussing the aesthetic qualities of the works;
iv
standardized approaches to teaching limited what teachers could apply from their learning at the summer institutes; and that subtle power dynamics and tensions found in the participants’ school environments affected their curricula and restricted their ability to select specific types of professional development. The findings of this study suggest that professional development designed by art museum educators for in-service teachers should aim to strengthen instruction of Visual Thinking Strategies, focus more on how teachers can tackle socially challenging topics, provide more instruction on how to incorporate aesthetics into a discussion, offer more follow-up with the in-service teachers once the program has completed, and consider other channels to provide professional development for in-service teachers who cannot attend art museum summer institute programs.


identifier: Haghbin, Negar  (2020) Notification, gamification and cueing in mobile health applications.  Masters thesis, Concordia University.

abstract:
Lack of physical activity is prevalent in today’s society despite the fact that not getting enough physical activity contributes to different physical and mental conditions. Exercises are also used as physical therapy for various injuries and disabilities. Yet, many people do not commit to regular exercise routines or frequently do not adhere to their rehabilitation routines due to difficulties with scheduling regular appointments with professionals, the high expenses of personal trainers and physical therapists, and even just boredom with the prescribed regimens.
In this dissertation, we first studied the impact of push notifications and gamification on people’s motivation for doing regular exercises. To do this we developed a fitness mobile application, 5 mins with the ability to send custom reminder push notifications. In a preliminary study, we evaluated the effectiveness of reminder push notifications on performing fitness exercises.
In addition, we investigated the effect of multimodal cueing mechanisms on physiotherapy exercises. We developed a wrist rehabilitation mobile application that works with the NeblinaTM wearable sensor. A user study with 10 users was done to assess the impact of gamification and cueing on subjects’ performance of basic wrist rehabilitation exercises.
The result of our experiments shows the importance of fine-tuning both notifications and cueing strategies in exercise and rehabilitation. Whereas well-timed notifications and multimodal cueing can provide motivation and help encourage an end-user, poor methods may discourage users from being active and performing their exercises correctly.


identifier: Kizhakkan, Akhil Raj  (2020) Optimal Electric Vehicle Charging Station Location Allocation using Agent-Based Modeling and Simulation: A case study of city of Montreal.  Masters thesis, Concordia University.

abstract:
Widespread acceptance of all electric vehicles faces two major challenges. First being the higher price tag compared to a similar utility IC engine vehicle, while giving equal or lesser range. Second being the under-developed infrastructure support for refueling. Current trends in Electric Vehicle (EV) industry shows an increase in battery capacity and higher charging speed capabilities owing to an increased adoption of EVs. This thesis focuses on the second challenge of range anxiety of EV users due to lack of enough charging infrastructure compared to their gasoline powered counterparts. Public fast charging infrastructure is proposed as the solution to solve range anxiety and wider acceptance of EVs by public. As setting up the public charging stations at the initial stages of Electric Vehicle (EV) market penetration can be budget demanding, it is therefore crucial that the locations chosen should cover maximum demand at least cost and best convenience.
This thesis discusses the review of research publications focused on optimal placing of Alternative Fuel/Electric Vehicle Charging Stations (EVCS), by considering various approaches and models they have used. Heuristic methods of solving optimization problems was given an additional focus in the review. This thesis addresses the discrete, multi-objective, capacitated location allocation problem of electric vehicle charging station, using agent-based modeling. The developed model of EV trips in an urban environment and their charging events was fed with real data from city of Montreal, allowing the model to replicate real charging demand situations at the charging stations at various locations across the city of Montreal. A pareto optimization method is developed using a simple evolutionary genetic algorithm to find all the best trade-offs between each objective value. The multiple objectives considered are utilization of charger resources, the average reroute distance of EVs to reach a charging station and number of infeasible trips and optimization is done through running the agent-based model iteratively through the genetic algorithm, evolving its solutions in each iteration. Proposed solutions for each optimal objective value and solution with the best trade-off between the objectives are discussed.


identifier: Shahtalebi, Soroosh  (2020) towards effective application of data-driven learning models for assistive technologies and brain-computer interfaces.  PhD thesis, Concordia University.

abstract: The worldwide population of seniors (60+) is expected to increase from 962M in 2017 to 2.1B in 2050 and 3.1B in 2100. Thus, a parallel rise in a range of age-related disorders and diseases including Parkinson’s Disease (PD) and Essential Tremor (ET) is expected. Pathological Hand Tremor (PHT) is a common symptom of such disorders, which severely affects patients’ quality of life. There are more than 100,000 Canadians living with PD today, but that number is expected to jump over the coming decade to more than 160,000, which necessitates an urgent quest for thoughtful planning and proactive measures. The development of assistive and rehabilitation technologies is one approach that stands to help affected individuals compensate for a variety of lost functionalities, and regain their self-sufficiency. One promising direction is to introduce a new communication medium to the human brain, known as Brain-Computer Interfacing (BCI), to bypass the impaired neural pathways and provide a direct link between the brain and an Assistive Device (AD). In addition, another approach is to capitalize on the remaining functionalities of the patients and compensating for the lost capabilities or correcting the flawed ones. In this dissertation, various types of signal processing and machine learning frameworks are developed to be utilized in the above-mentioned ADs, each enhancing our understanding of the problem at hand and surpassing its counterparts in terms of estimation accuracy, classification accuracy, adaptivity, and generalizability. In particular, a Bayesian optimization framework and an innovative multiclass classification scheme are developed to enhance the classification accuracy of BCI systems. In addition, two processing frameworks based on Adaptive Kalman filtering and Recurrent neural networks are introduced, which drastically enhance the accuracy of PHT estimation from the dynamics of tremorous hands. Moreover, a comprehensive screening protocol based on Convolutional Neural Networks is proposed for the diagnosis of PD from ET, which offers the state-of-the-art accuracy compared to its counterparts. Finally, a novel objective function for deep metric learning frameworks is developed to lessen the necessity of huge datasets to train artificial neural models in the biomedical domain, and in particular for neurological disorders. Such advancements would not only minimize the caregiving burden but could also help increase the number of productive and self-sufficient years patients have before debilitating disease symptoms take over.


identifier: Zhang, Zhen  (2020) Exploring the Impact of Visual Information on Ethnic Restaurants Purchase Intention in Food Ordering Apps and the Moderating Role of Reputation, Cosmopolitanism and Cultural Values.  Masters thesis, Concordia University.

abstract: Food ordering apps (FDAs) have gained popularity in recent years due to the rapid development of the internet and mobile technology. Especially during the COVID-19 pandemic, more customers choose to order deliveries from restaurants using FDAs. This study examined how visual information, specifically, the pictures of the dish that appeared on the ethnic restaurant menu in FDAs impact consumer purchase intentions through trust and attitudes. The moderating effect of cosmopolitanism and cultural values were also explored. The studies were conducted cross-culturally in both North America and China to test consumers’ purchase intention towards a Japanese restaurant in an FDA. Results revealed that consumers’ purchase intention towards ethnic restaurants was positively affected by visual information presented in FDA menu through the serial mediation of online trust and attitudes. Cosmopolitanism was found to significantly moderate the positive relationship between visual information and online trust. Uncertainty avoidance, one of the six dimensions of cultural values, was also found to moderate the positive relationship between online trust and attitudes. Furthermore, the theoretical and managerial implications of the findings are discussed.


identifier: Mohammadi, Sadaf  (2020) Particle Image Velocimetry of Suspension Plasma Spray in the Vicinity of a Substrate.  Masters thesis, Concordia University.

abstract:
Thermal spray technology is a widely used technique in the industry in which surfaces of components are coated by spraying a wide range of metals or ceramics. Considering the growing interest
in building nanostructured coatings due to their unique characteristics, a new technique called suspension plasma spraying is developed. Suspension plasma spraying (SPS) is one of the promising
methods that can be used to achieve coatings with fine microstructure and superior properties. Several significant parameters make this SPS process complex to understand and control. In particular,
the velocity and diameter of in-flight particles near the substrate, which depend on the plasma and
injection conditions, have a strong influence on the coating columnar structure. In this work, the
velocity of in-flight particles in the vicinity of the substrate and free jet was investigated by the
particle image velocimetry (PIV) method. This study is aimed at providing a better understanding
of the behavior of in-flight particles in the SPS process. A suspension of titania particles is used
here taking advantage to the abundance and chemical stability of titania and its use in different
SPS applications. Observations and results of this work showed that the trajectory and velocity of
micron-size particles, due to their small Stokes number, are strongly influenced by the presence of
the substrate. The PIV results make it possible to visualize the suspension injection into the plasma
with titania aqueous suspension. Particle velocity reported by PIV method corresponded likely to
un-molten particles in SPS process. The PIV measurements were finally compared with velocity
measurements carried out with a thermal-emission particle sensor in the same spray condition as
the PIV run.


identifier: Chisholm, Alexandra  (2020) The role of the paraventricular nucleus of the thalamus in the augmentation of heroin seeking induced by chronic food restriction.  PhD thesis, Concordia University.

abstract: North America is in the midst of an opioid crisis. Many individuals who suffer from addiction to opioids attempt to stop using the drug. However, many of these individuals often find it difficult to maintain abstinence and ultimately end up relapsing, starting the vicious cycle of addiction all over again. In both human and animal models of drug addiction, chronic food restriction increases rates of relapse. Our laboratory has previously demonstrated a robust increase in drug-seeking following a period of abstinence in chronically food-restricted rats compared to their sated counterparts. To date, the neural mechanisms that mediate the effect of chronic food restriction on drug-seeking remain elusive. However, the paraventricular nucleus of the thalamus (PVT) appears to be a promising candidate to investigate. The PVT is uniquely situated to contribute to homeostatic and drug-seeking systems. Thus, this thesis aimed to examine the role of the PVT in the augmentation of heroin seeking induced by chronic food restriction using a chemogenetic approach. In the first series of experiments, we sought to determine if the PVT plays a role in heroin seeking in chronically food-restricted rats following a period of abstinence. Here, we showed that chemogenetically activating the PVT abolishes heroin seeking in chronically food-restricted rats indicating that the PVT does play a role in this phenomenon. Next, we investigated the role of the input from the prelimbic cortex to the PVT. Here, we did not observe any changes in heroin seeking behaviour in chronically food-restricted or sated rats when we activated or inhibited this pathway despite verifying that chemogenetic manipulations were sufficient to alter neuronal activity. In the last series of experiments, we assessed the role of the output from the PVT to the nucleus accumbens (NAc). We demonstrated that chemogenetically activating PVT projections to the NAc shell, but not core, abolished the augmentation of heroin seeking in chronically food-restricted rats. Taken together, the findings presented in this thesis indicate that the PVT and its projection to the NAc shell play a critical role in the regulation of heroin seeking in chronically food-restricted rats following a period of abstinence.


identifier: Varo, Quentin  (2020) Dynamic Reduced-Round TLS Extension for Energy-Saving Encryption in Wireless IoT Communications.  Masters thesis, Concordia University.

abstract: Securing the wireless Internet of Things (IoT) is a complex challenge due to devices’ computing capacity limitations, battery restrictions or insufficient power supply. Reaching 30 billion connected devices in 2020, the IoT sector is booming. According to marketing studies, by 2025, the global IoT market is expected to reach $34.4 billion and the global IoT battery market is estimated to growth to $15.8 billion. Nevertheless, the smart city, connected healthcare, Industry 4.0 and home security, representing over 75% of the IoT market, raise critical cybersecurity and energy consumption issues. The battery lifespan of specific devices such as Wireless Sensor Networks (WSNs), Wearable or Implantable Medical Devices (WMDs, IMDs) can then be drastically impacted. To meet emerging demands, new solution to provide both cybersecurity and energy efficiency must be developed. Hence, this thesis research tried to develop a dynamic and secure solution to balances communication security and power consumption according to the IoT device's current battery level and the reduced-round cryptography. The contributions are as follow: (1) the security and power consumption evaluation of reduced-round cryptography on different lightweight ciphers; (2) the design, and implementation of a dynamic mechanism to control the battery discharge by adjusting the communication encryption cipher reduced-round value; (3) the design, integration and evaluation of  our dynamic reduced-round mechanism integrated within TLS protocol version 1.2 and 1.3. The results of the two experiments confirm the efficiency of the reduced-round cryptography and of our dynamic round-reduced TLS extension to achieve a trade-off between IoT's communications security level and energy savings.


identifier: Zhang, Yongxuan  (2020) Domain Adversarial Transfer Learning for Robust Cyber-Physical Attack Detection in the Smart Grid.  Masters thesis, Concordia University.

abstract:
Thanks to the increasing availability of high-quality data and the success of deep learning algorithms, machine learning (ML)-based classifiers have become increasingly appealing and investigated against sophisticated attacks in complex cyber-physical systems like the smart grid. However, many of these techniques rely on the assumption that the training and testing datasets share the same distribution and class labels in a stationary environment. As such assumption may fail to hold when the system dynamics shift and new threat variants emerge in a non-stationary environment, the capability of trained ML models to adapt in complex operating scenarios will be critical to their deployment in real-world applications. Using cyber-physical attack detection in the smart grid as the targeted application, this research aims to leverage transfer learning-based strategies to improve the robustness of ML classifiers against variations in threat types, locations, and timing in a complex dynamic CPS.
To this end, this research investigates and develops domain-adversarial transfer learning schemes for robust intrusion detection against smart grid attacks.
The main contributions include: (i) A domain-adversarial transfer learning scheme with customized classifiers for attack detection based on realistic smart grid data collected from a hardware-in-the-loop testbed; (ii) A semi-supervised transfer learning to transfer
the knowledge of limited known attack incidences to detect returning threats at a later time with different system dynamics; (iii) A divergence-based transferability analysis and a spatiotemporal domain-adversarial transfer learning scheme for robust detection against spatial and temporal variants. Experiments were conducted on standardized IEEE benchmarks, and the results have demonstrated the promising capability of domain adversarial transfer learning to improve ML robustness against system and attack variations.


identifier: Bergeron, Annie  (2020) Second language speakers’ attitudes towards the Québec French variety: An exploration of urban and rural Hispanic speakers.  PhD thesis, Concordia University.

abstract: The province of Québec has been the scene of great political and linguistic changes in the past 60 years. These changes have often challenged Québec residents to reassess their existing or to develop new attitudes towards language and language use, and towards various ethnolinguistic groups, including French- and English-speaking communities. Despite its status as the sole official language of Québec, French—and more specifically the Québec French (QF) variety—has frequently been relegated to a lower status in favor of the Parisian “norm,” which is usually referred to as French from France (FF). Designated as the target variety for Radio-Canada newscasters, FF is also often adopted by teachers of French as a second language (L2) in Québec’s classrooms. It is little surprise, therefore, that L2 speakers of French tend to give negative evaluations to speakers of QF while not being able to reliably distinguish between the two varieties. However, far less is known about L2 speakers’ attitudes towards the QF variety specifically, and the French variety that they would like to learn and use. There is also a lack of research examining the effects of L2 speakers’ attitudes on their production of QF speech patterns, in relation to their sense of belonging towards the majority (francophone) community. But most importantly, there is a need to investigate the attitudes that L2 speakers living outside urban centres hold towards the QF variety and its speakers, and to examine whether these attitudes change, particularly in contexts where L2 speakers such as foreign temporary workers—an essential labour force representing more than 12,000 Latin Americans yearly—are geographically and socially isolated. With a better understanding of these issues, it would be possible to create practical recommendations for L2 French teachers as well as employers and professionals in charge of hiring foreign workers. With these broad goals in mind, this doctoral dissertation includes three studies, all carried out from a sociolinguistic perspective. Study 1 investigated L2 French speakers’ attitudes towards the QF speech variety as a function of their participation in the francisation (French-language instruction) program. Fifty-eight adult L2 French speakers listened to short sentences that either included or did not include QF speech patterns, rating these sentences for pleasantness, extent of their exposure to these and similar pronunciation patterns, and their preference to choose these patterns as a pronunciation model to follow. Focusing on the same 58 L2 French speakers, Study 2 examined the links between these speakers’ acculturation towards their home culture and the Québec culture and their preference for and their production of QF speech features, separately for those with and without experience in the francisation program. Finally, Study 3 targeted a group of 12 Guatemalan temporary workers living in rural areas, investigating longitudinally their attitudes and motivations towards learning and using L2 French, with qualitative analyses conducted to determine whether there was a change in the workers’ language use, attitudes, and motivation levels throughout their work experience in a French-speaking environment.


identifier: Liu, Lili  (2020) Computerization of the Wartegg Test in Handwriting Analysis.  Masters thesis, Concordia University.

abstract: Wartegg Test (WT) is a drawing completion task designed to reflect the personality characteristics of testers. WT is a functional and usable psychological test which is not yet well known due to the language and region barriers among worldwide researchers. It is urgent to automate WT’s psychological analysis process to let more people realize and use it, especially during the Covid-19 pandemic period, when people are asked to stay at home for a long time with limited access to in-person psychology consulting. The computerized WT allows people to attend psychology tests at home without taking the risk of virus infection. In this thesis, we proposed a system of computerizing the Wartegg test in handwriting analysis. We have implemented the image processing methods and machine learning algorithms on the Wartegg test to construct a personality analysis tool, which could mimic the evaluation process of psychologists automatically. We have extracted five features based on WT theory, they are space utilization, the numerical order of sequence, line curve ratio, animate or inanimate classification and category classification. After calculating all five features, we added them together and projected the summarized result to the Big five personality traits to get the final result. This system allows people to attend the Wartegg test at home and get the result immediately. The processing of the system has nothing to do with language and region, and only analyzes and obtains results based on drawings. Hopefully, this work could facilitate people to attend the Wartegg psychologist test with privacy security.


identifier: Dong, Jinxin  (2020) Development of a Regional-Scale Numerical Environmental Multimedia Modeling (RNEMM) Approach to Assess Spatial Eco-Environmental Exposure Risk of Contaminants.  PhD thesis, Concordia University.

abstract:
Environmental multimedia models (EMMs) have been used in environmental risk assessment since they reflect the interactive transport and fate processes of chemicals within multi-compartmental environmental systems. However, these existing models are limited in their abilities to predict contaminant concentrations in the multimedia environment at a better temporal-spatial resolution, especially for regional scale site contamination problems.
A novel regional numerical multimedia environmental modeling (RNEMM) approach is developed in this thesis for the multimedia environment. RNEMM consists of a water quality simulation module for river network system, a gaseous phase simulation module, a mass balance analysis module for soil, a food web module and a health risk assessment module. Two key developments are achieved to integrate these modules into the entire system: integrating different modules with non-uniform boundary conditions and inter-media flux, and coupling the modules based on practical considerations of regional scale multimedia contamination problems. Importantly, four validations related to the RNEMM development are conducted. First, a new model in 1D and 2D are established through considering the non-uniform boundary conditions and inter-media flux. Numerical algorithms, including the finite element method (FEM) and finite volume method (FVM), are used to solve the governing equations. The measured results in two landfill cases are used to validate the spatial and temporal accuracy of predicted concentrations obtained from these models. Second, a regional spatial environmental multimedia model (RSEMM) is developed by coupling the modules based on a different concept (i.e., a water quality model for the river network and a fugacity model) to provide predicted concentration profiles of contaminants at a regional scale. A third regional site validation is conducted through a case in the urban area of Tianjin City and reasonable results have been obtained through the comparison of simulated and measured results.
Subsequently, based on the two key developments, a regional numerical multimedia environmental modeling (RNEMM) approach is developed and tested by assessing spatial eco-environmental exposure risk of perfluorooctane sulfonate (PFOS) in the pearl river basin. The exposure risk assessment for children and adults is further conducted based on the integrated risk assessment model. In conclusion, the RNEMM can serve as a comprehensive management tool to assess and control the exposure risks associated with emerging contaminants on regional water, soil, and ecosystem at an adequate spatial-temporal resolution.


identifier: Mazloomi, Anahita  (2020) Reinforcement Learning Frameworks for Server Placement in Multi-Access Edge Computing.  Masters thesis, Concordia University.

abstract:
In the IoT era and with the advent of 5G networks, an enormous amount of data is generated, and new applications require more and more computation power and real-time response. Although cloud computing is a reliable solution to provide computation power, the real-time response is not guaranteed. Thus, the multi-access edge computing (MEC), which consists of distributing the edge servers in the proximity of end-users to have low latency besides the higher processing power, is increasingly becoming a vital factor for the success of modern applications.
Edge server placement and task offloading play a crucial role in the efficient design of MEC architecture. There is a finite discrete set of possible solutions, and finding the optimal one is known to be an NP-hard combinatorial optimization problem. Heuristics, mixed-integer programming, and clustering algorithms are among the most widely used approaches to solve this problem.
Recently, researchers have investigated reinforcement learning (RL) to solve combinatorial optimization problems, which has shown promising results. In this thesis, we propose novel RL-frameworks for solving the joint problem of edge server placement and base station allocation. There are a few studies that have used RL in placement optimization. In our investigation, the focus is on the modeling part to make the Q-learning applicable for a large scale real-world problem.
Therefore, in this research, Q-learning is examined and applied in the edge server placement while considering two significant and striking perspectives. The first one is about minimizing the cost of network design by reducing the delay and the number of edge servers. The second perspective is the placement of K-edge servers to create K-fair-balanced clusters with minimum network delay.
Despite the impressive results of RL, its application in real-world scenarios is highly challenging. Throughout our modeling, the faced issues are explained, and our solutions are provided. Besides, the impact of state representation, action space, and penalty function on the convergence is discussed. Extensive experiments using a real-world dataset from Shanghai demonstrate that in the light of efficient penalty function, the agent is able to find the actions that are the source of higher delayed rewards, and our proposed algorithms outperform the other benchmarks by creating a trade-off among multiple objectives.


identifier: Guédon, Timothée  (2020) A heuristic to repartition large multi-dimensional arrays with reduced disk seeking.  Masters thesis, Concordia University.

abstract: Multi-dimensional arrays have become critical scientific data structures, but their manipulation raises performance issues when they exceed memory capacity. In particular, accessing specific array regions can require millions to billions of disk seek operations, with important consequences on I/O performance. While traditional approaches to address this problem focus on file format optimizations, we are searching for algorithmic solutions where applications control I/O to reduce seeking. In this thesis, we propose the keep heuristic to minimize the number of seeks required for the repartitioning of large multi-dimensional arrays. The keep heuristic uses a memory cache to reconstruct contiguous data sections in memory. We evaluate it on arrays of size 85.7 GiB with memory amounts ranging from 4 to 275 GiB. Repartitioning time is reduced by a factor of up to 2.5, and seeking is reduced by four orders of magnitude. Due to the effect of asynchronous writes to memory page cache enabled by the Linux kernel, speed up is only observed for arrays that exceed the size of working memory. The keep heuristic could be applied in platforms that manipulate large data arrays, as is commonly the case in scientific imaging.


identifier: Heydari, Ali  (2020) Three Essays on the Role of Indulgence vs. Restraint in Consumer Behavior.  PhD thesis, Concordia University.

abstract: The purpose of this thesis was to conceptualize and develop a valid and reliable scale for the sixth dimension of Hofstede's cultural framework (Hofstede 1980; 2001; Hofstede et al. 2010), indulgence vs. restraint, at the individual level. It also examined the impact of individual-level indulgence vs. restraint on two areas of consumer behavior, hedonic-utilitarian attitudes and vice-virtue consumption. Despite the importance of indulgence vs. restraint culture influence on consumer behavior and human behaviors, little research has been done to measure this dimension at the individual level. Essay 1. Following Churchill's (1979) approach, a first data collection served to reduce an initial pool of 40 items to a final 6-item scale for indulgence vs. restraint at the individual level. A second data collection confirmed the strong reliability of the scale. Essay 2. Through an online survey using three different contexts (restaurant re-visited, car or cellphone repurchase) to capture the variance, the results demonstrated the nomological validity of the construct at the individual-level indulgence. The hedonic and utilitarian attitudes mediated the impact of individual-level indulgence on repurchase intentions and WOM intention. The impact of individual-level indulgence on hedonic and utilitarian attitudes was also mediated by positive post-purchase emotions (confidence and delight). Essay 3. Using an online survey, we demonstrated that the scale had a significant correlation with constructs associated with indulgence vs. restraint, establishing the criterion validity of the scale. We investigated the influence of individual-level indulgence on vice and virtue consumption behavior in the context of food consumption. The results of binary logistic regression showed that indulgence vs. restraint was a strong predictor of preference between vice and virtue (for all three pairs of snacks used in the study). Compared to low-indulgence people, high-indulgence people were more likely to choose vice over virtue. Essays 1 to 3 demonstrated the individual-level indulgence scale's convergent validity and the discriminant validity against hyperopia and self-control.


identifier: Boos, Thomas (Alex)  (2020) From Turtle Island to France: The Kanata Controversy and Decolonial Translation Practices.  Masters thesis, Concordia University.

abstract: In 2018, Québécois playwright Robert Lepage and the French stage director Ariane Mnouchkine were in the spotlight of a transatlantic controversy over their theatrical coproduction entitled Kanata. While Anglophone and Francophone news outlets in Quebec/Canada/Turtle Island and in France tended to explain away the controversy in terms of a conflict between cultural appropriation and artistic freedom of expression, this thesis alters that discourse by re-articulating the controversy in more complexity from a translation studies perspective. The first sequence focuses on the evolution and production of the plays Kanata and ¬Kanata – Épisode I – La Controverse to stimulate reflection on what might constitute decolonial theatre translation and/or adaptation practices. The second sequence shifts focus onto the transatlantic media controversy. Selected publications produced by news outlets based in Quebec/Canada/Turtle Island and in France are analyzed to outline their differential writing practices. Subsequently, I chart out the conditions of possibility of these writing practices by examining them within their contexts of production. This is followed by a comparison of Indigenous, settler and European receptions of Kanata – Épisode I – La Controverse. The divergent writing practices and discursive lacunae that are traced out between the contexts of Quebec/Canada/Turtle Island and France are attributed to knowledge gaps in France on Indigenous Peoples and the history of settler colonialism in the Quebec/Canada/Turtle Island context. The third and last sequence seeks to respond to these knowledge gaps with decolonial translation flow models and guidelines for decolonial and decolonizing translation practices in effort to consolidate Indigenous-settler solidarity politics and alter social consciousness around these issues in France. / En 2018, le dramaturge québécois Robert Lepage et la metteuse en scène française Ariane Mnouchkine se sont retrouvés au cœur d’une controverse transatlantique centrée sur leur coproduction Kanata. Alors que les médias anglophones et francophones au Québec/Canada/Île de la Tortue et en France tendaient à expliquer la controverse par une analyse en termes de conflit entre l’appropriation culturelle et la liberté d’expression artistique, la présente recherche modifie ce discours en réarticulant la controverse dans sa complexité d’un point de vue traductologique. Dans un premier temps, la recherche se focalise sur l’évolution et la production des pièces de théâtre Kanata et Kanata – Épisode I – La Controverse dans le but de susciter la réflexion sur la constitution des pratiques de traduction et d’adaptation décoloniales des œuvres de théâtre. Dans un deuxième temps, la recherche réoriente l’attention sur la controverse médiatique transatlantique. Je soumets une sélection de publications journalistiques produites par des médias au Québec/Canada/Île de la Tortue et en France à une analyse discursive afin d’exposer leurs différentes pratiques d’écriture. Ensuite, je résume certaines conditions de possibilité de ces pratiques d’écriture en les analysant dans le contexte de leur production. Cette analyse est suivie par une comparaison des réceptions de Kanata – Épisode I – La Controverse par les autochtones, allochtones  et Européens. Les pratiques d’écriture et les lacunes discursives entre le Québec/Canada/Île de la Tortue et la France sont attribués à des écarts de connaissance en France quant aux peuples autochtones et à l’histoire du colonialisme de peuplement dans le contexte de Québec/Canada/Île de la Tortue. Dans un troisième et dernier temps, la recherche vise à répondre aux écarts de connaissance en proposant des modèles de flux de traduction décolonial et en fournissant des pistes décoloniales et décolonisatrices pour la pratique de la traduction dans le but de consolider l’esprit solidaire entre les autochtones et allochtones/colons dans les luttes politiques et de conscientiser la France sur ces luttes et enjeux.


identifier: Nair, Dezaye  (2020) Handing the Mic to the Mosaic: Lessons on Race from Canadian Music Artists of Colour.  Masters thesis, Concordia University.

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abstract: One of Canada’s most notable traits as a nation-state is its policy of multiculturalism or its national identity as a “cultural mosaic”. In this research-creation project, 11 music artists of colour from Montreal, Toronto and Vancouver were asked how multiculturalism affects their cultural identities and music-making practices. This research builds on existing work that has lauded music as a valuable site for studying race relations within Canada. Investigating Canadian multiculturalism as an active political ideology, this project comprises both a written analysis and podcast series. Multiculturalism is mediated diversity which distracts away from the inherent whiteness at the centre of the colonial settler-state. As an ideology and dominant discourse, it filters down into infrastructures and everyday interactions. This project points to some of the ways that this occurs for artists of colour, namely through the static and single-layered dimensional framings of their work. On top of this, this research shines a light on how discourses around traits for a sellable and successful music, act i.e. “authenticity” and “relatability”, are also informed by this brand of Canadian race politic. More importantly, this thesis showcases how artists in their music-making practices navigate these nuanced barriers as well as how their works and perspectives offer robust alternate understandings to state multiculturalism.


identifier: Pérez Martínez, Bettina  (2020) Climate Change and Hurricanes: An Ecocritical and Decolonial Analysis of the Work of Puerto Rican Visual Artists Frances Gallardo and Lionel Cruet.  Masters thesis, Concordia University.

abstract: On September 20, 2017 Hurricane María made landfall on the archipelago of Puerto Rico, causing one of the most devastating hurricane-related humanitarian crises since 1928,  bringing to light the accelerated impact of climate change in Puerto Rico and throughout the Caribbean region. This thesis explores how the work of contemporary Puerto Rican artists Lionel Cruet and Frances Gallardo addresses hurricanes, climate, and the environment in Puerto Rico, while pointing to the interconnected forces of colonialism and capitalism.  The destruction caused by Hurricane María is approached through Puerto Rico’s relationship with the United States, and analyzed from a decolonial perspective that is rooted in critical Caribbean studies. This thesis also draws on ecocritical and Anthropocene-related art historical scholarship, to position the critical art practices of Cruet and Gallardo in relation to the climatological, economic and socially-devastating bonds that tie Puerto Rico to the United States.


identifier: Blanchard, Marie-Josée  (2020) Tasting Physical Expression: A Sensorial and Cultural Analysis of the Notion of Rasa in Classical Indian Dance.  PhD thesis, Concordia University.

abstract:
This dissertation is a detailed study of the notion of “rasa,” a Sanskrit term that translates as “juice,” “essence,” or, in the performing arts, “aesthetic delight.” The research has two goals: (1) to determine what rasa means within Indian neo-classical dance, especially in Eastern Canada (Quebec and Ontario), by contrasting the classical theory of rasa to its present actualization in the performing arts through foundational moods and emotions (erotic love, mirth, repugnance, terror and so on); (2) to provide a more complex understanding of the senses and emotions in Indian society and beyond through a sensory and affective anthropological analysis of rasa. Data for this project was collected from interviews with Montreal- and Toronto-based professional Bharatanatyam and Odissi dancers as well as participant sensation in India and Canada. The data was complemented by the study of (translated) Sanskrit theory on rasa (Natyashastra, Abhinaya Darpana, works of theorists Anandavardhana, Abhinavagupta, Bhojaraja, Rupa and Jiva Goswamin, etc.).

The research shows that rasa, understood as an experience in the viewer, is not limited to the expert spectator (rasika) as classical theory would have it, but rather expands to the performer and emerges out of the relationship that is built between dancer and audience (including uninitiated spectators). Rasa, in fact, is an aesthetic experience that can be learned by performers and spectators—a process coined as “rasik literacy.” Spectators develop their rasik literacy through the exposure to the aesthetics of Indian performing arts via visual, imaginative, aural and aesthetic savouring. In addition to these, dancers acquire theirs through internal perception such as kinaesthesia, proprioception, equilibroception, (embodied) rhythm and flow, thus forming body memories that are eventually associated with the emotional hues of rasa. Hence, rasa is a rich emotional, sensorial and aesthetic concept that greatly enriches the field of sensory anthropology and demonstrates the inadequacy of the mind-body opposition in the study of cultures. As an “embodied thought” (Michelle Z. Rosaldo), this concept also expands the understanding of the aesthetics reception of emotions in the performing arts by inviting spectators and artists to taste performances, rather than just looking or listening to them.


identifier: Simpson, Shawn  (2020) A Pilot Study of the Barbadian Reef Microbiome: New Approaches for Comparative Analyses.  Masters thesis, Concordia University.

abstract: Coral reef systems are fundamentally important ecosystems for the island of Barbados, supporting a highly diverse marine fauna along while providing critical resources and services which residents of the island are dependent on through means such as food and employment. Coral reefs also play a key role in the contribution to the economies of the Caribbean small island developing states such as Barbados, where tourism is the main economic driver. Over the past five decades the Barbadian coral reef systems have been impacted by global (eg climate change) and local stressors (eg anthropogenic runoffs) causing dynamic changes in the ecosystems such as coral bleaching and benthic algal domination, leading to loss of coral cover and an increase in coral mortality. Here we conducted a pilot study into the marine microbial communities that inhabit the seawater of two coral reef systems located on the west coast of Barbados that have different structural features, ecological features and local stressor exposure. By incorporating modern and novel analysis approaches we revealed that the two reef systems have distinct microbial ecology compositions that reflect the ecosystems ecological differences and effects by stressors. Our data gives insights into the microbial microbiome that interacts with other microbiomes (eg coral holobiont) within the Barbadian reef ecosystems, creating a baseline for future studies and surveying efforts.


identifier: Eltoukhy, Mohamed Nabil Abdelghaffar ORCID: https://orcid.org/0000-0003-1844-7030 <https://orcid.org/0000-0003-1844-7030>  (2020) Algorithms for Tracking Single Maneuvering and Multiple Closely-Spaced Targets.  PhD thesis, Concordia University.

abstract:
Target tracking is crucial in monitoring and controlling air traffic in civilian and military applications. Target tracking is a process of estimating the current position and predict the future position of one or more targets using the measurements received by a radar system.
One of the major challenges in tracking a single target is when it performs a maneuver and the angle of maneuver is not known. The interacting multiple model (IMM) algorithm is the most commonly-used algorithm for tracking a maneuvering target with an a priori knowledge of the target turn rate, since it provides a very good tracking performance with moderate complexity. However, the tracking performance of such an algorithm deteriorates or may even fail when the target performs a maneuver with a turn rate larger than that assumed in the design of the algorithm. A few methods have been reported to overcome this limitation of an assumed turn rate by actually estimating it adaptively. Two of such algorithms use nonlinear filters that leads to a large complexity, and one of them uses linear filters and models providing good tracking performance, but only for mild maneuvers.
For tracking multiple targets, several algorithms have been proposed, among which the joint probability data association (JPDA) algorithm is considered to be the best algorithm, since it provides good tracking performance when the targets are widely spaced. However, the tracking performance of this algorithm deteriorates, and coalescence of the tracks may occur, when the targets are closely spaced. Some efforts have been made to overcome the problem of tracking closely spaced targets by ignoring the target identity, but at the expense of very large complexity.
The work of this thesis is carried out in two parts. In the first part, two algorithms within the IMM framework are proposed to track a single maneuvering target, when the target turn rate is not known a priori. In both the algorithms, the turn rate is dynamically estimated using noisy measurements. In the first algorithm, the turn rate at each time instant k is estimated based on the target speed and the radius of the circle formed by the measurement at that instant and the two previous consecutive noisy measurements, (k-1) and (k-2). The segment of this circle covered by these three noisy measurements is used to model the true track of the target at the instant time k. In the second algorithm, the accuracy of the turn rate estimated in the first algorithm is improved using the information on the level of the measurement noise.
In the second part of the thesis, a systematic study on the impact of the spacing between the targets as well as when the targets make abrupt turns with sharp angles on the tracking performance of the JPDA algorithm is conducted. Then, a new algorithm for tracking multiple targets based on the spatial distribution of the measurements for determining the weights for measurement-target association is proposed within the JPDA framework. The proposed algorithm for multiple target tracking is designed to deal with the problems of closely spaced targets and their abrupt sharp turns more effectively.
Effectiveness and superiority of the algorithms proposed for tracking single and multiple targets are demonstrated through extensive experiments with a wide variety of different scenarios for target motions.


identifier: Wolanski Brito, Beatriz  (2020) Us and Them, Now and Then: A historical comparison of the representation of diversity and gender equality in Québec’s elementary school textbook illustrations.  Masters thesis, Concordia University.

abstract:
This research project investigates how diversity and gender equality have been represented in textbook illustrations in the Canadian province of Québec. It will compare illustrations produced in the 1960s, when the province went through one of its most significant cultural shifts, known as the Quiet Revolution, with those produced in the 2010s. The objective of this investigation is to see how cultural shifts can be perceived in the form of visuals intended for elementary school children and how they work as a window to the understanding of a changing society. Representation, feminism, immigration, tolerance and acceptance are recurrent themes discussed in this project.
Also, this project aims to bring the discussion of the use of illustration for educational purposes to the field of design. A recent surge in research papers that debate the importance of diverse literature and textbooks for children shows an increasing interest in the topic. However, the scholars writing those papers belong to the fields of the social sciences, child psychology and pedagogy, but not to design.
This project advocates for designers’ active participation in building knowledge about diversity in children’s literature and textbook illustration. Considering how designers have an active role in the production of books and how papers analyzing diversity in children’s books may at some point critique the outcome of graphic designers’ and illustrators’ work, research from a design perspective becomes necessary.
Keywords: textbook, graphic design, illustration, education, diversity, multiculturalism


identifier: Abdel Malek, Kenzy  (2020) Computability of Function Spaces from Harmonic Analysis.  Masters thesis, Concordia University.

abstract:
In computer science and mathematics, a computable function is one for which a computer program exists and can give its values in finite time. We explore notions of computability for function spaces such as the Hardy space H^1(R) and the Besov space B^p(T) from harmonic
analysis. After a comprehensive introduction to computable analysis and studying several function spaces, we establish some original results. Namely, that there exists a dense computability structure on the Besov space.


identifier: Karimi, Raika  (2020) Low Fatigue Designs and Deep Learning-based Classification for Motion Visual Evoked Potentials.  Masters thesis, Concordia University.

abstract: Recent advancements in Electroencephalography (EEG) sensor technologies, Signal Processing (SP), and Machine Learning (ML) algorithms have paved the way for further evolution of Brain Computer Interfaces (BCI) in several practical applications ranging from rehabilitation systems to smart consumer technologies. In particular, this thesis research is motivated by potentials of BCI platforms to provide comfortable means for individuals with communication disabilities to interact with the outer world. When it comes to SP/ML models for BCI systems, there has been a surge of interest on Visual Evoked Potentials (VEPs). Recently, Steady-state visual evoked potential (SSVEP) has become popular due to their fast and reliable performance, and strong provocation of visual brain signals.  Despite the popularity of SSVEPs, their utilization for practical applications especially for assistive technologies is challenging due to eye fatigue and risk of induced epileptic seizure. In this regard, the key issue of conventional light-flashing techniques has been addressed by development of flicker-free Steady-State motion Visual Evoked Potential (SSmVEP).  Such benefits, however, come with the price of having less accuracy and less Information Transfer Rate (ITR). In this regard, the thesis focuses on improving the following three main components: (i) Stimulation paradigm; (ii) Frequency modulation, and; (iii) Target classification in SSmVEP-based BCIs. With regard to the first component, novel SSmVEP paradigms with low luminance contrast and oscillating expansion and contraction motions are designed, and integrated within a BCI system. Through experimental evaluations, high detection accuracies are achieved for our proposed paradigms leading to less visual tiredness in comparison to conventional SSVEPs. Concerning the second component, an efficient modulation mechanism is proposed without using resources such as trial time, phase, and/or number of targets to enhance the ITR. The proposed design is based on the intuitively pleasing idea of integrating more than one motion within a single SSmVEP target stimuli, simultaneously. To elicit SSmVEP, we designed a novel and innovative dual frequency aggregated modulation paradigm, referred to as the  Dual Frequency Aggregated steady-state motion Visual Evoked Potential (DF-SSmVEP). The proposed DF-SSmVEP is evaluated based on a real EEG dataset and the results corroborate its superiority. With respect to the third component, it is expected that incorporation of human brain's nonlinear dynamics and characteristics of the designed videos within  our EEG signal classifier lead to a comprehensive model resulting in better noise removal. To this end, a deep learning-based classification model is proposed, referred to as the Deep Video Canonical Correlation Analysis (DvCCA), that extracts features of the SSmVEPs directly from the videos of stimuli. The proposed DvCCA is evaluated based on a real EEG dataset and the results corroborate its superiority against recently proposed state-of-the-art Convolutional Neural Network-based models.


identifier: Fiorito, Ulysses  (2020) Machiavelli’s Aesthetic Advice: Visual Arte and the Political Sublime in The Prince.  Masters thesis, Concordia University.

abstract: Machiavelli wrote The Prince with a keen eye for the realities of politics and would not have ignored an aspect as politically relevant as art. The visual arts were pervasive at all levels of state and society in 15th-16th century Florence. The Medici family’s conscious use of art as a means of securing political legitimacy and glory, along with Machiavelli’s involvement in the commissioning of Leonardo and Michelangelo’s Battle frescoes for the Florentine Republic, should lead one to consider the inclusion of advice on the use of visual art for political ends in The Prince. It is in the rhetorically distinct aesthetic representation of Remirro de Orco’s severed corpse in Chapter VII that one finds Machiavelli’s suggestion that a worthy prince must not only imitate Cesare’s violent and cunning virtù to craft an act of the political sublime, but also adopt his use of a visual effect for political legitimacy. Chapter XVIII further elaborates on this point through its emphasis on the appearances a prince can cunningly craft with the aid of artists and their arte so as to gain favor from the people and deceive political rivals - a point exemplified through Machiavelli’s use of Leon Battista Alberti’s language describing the visual arts. In both instances, a form of political legitimacy is attained through a visual effect inspired by a religious aesthetic. Arte, in these instances, goes beyond being a metaphor for statecraft, and becomes an actual strategic tool for governance in Machiavelli’s text.


identifier: kaur, bhupinder  (2020) an analysis of security vulnerabilities in container images for scientific data analysis.  Masters thesis, Concordia University.

abstract:
Software containers greatly facilitate the deployment and reproducibility of scientific data analyses on high-performance computing clusters (HPC). However, container images often contain outdated or unnecessary software packages, which increases the number of security vulnerabilities in the images and widens the attack surface of the infrastructure. This thesis presents a vulnerability analysis of container images for scientific data analysis. We compare results obtained with four vulnerability scanners,
focusing on the use case of neuroscience data analysis, and quantifying the effect of image update and minification on the number of vulnerabilities. We find that container images used for neuroscience data analysis contain hundreds of vulnerabilities, that software updates remove about two thirds of these vulnerabilities, and that removing unused packages is also effective. We conclude with recommendations on how
to build container images with a reduced amount of vulnerabilities.


identifier: Abla, Farida  (2020) Diasporic Iranian Women Life Writing: An Analysis Using a Transnational Feminist Lens.  PhD thesis, Concordia University.

abstract:
This thesis examines fourteen diasporic life writing in English by Iranian women who mainly reside in North America (the United States and Canada), with a particular interest in how and why they write about their lives. The women authors bring to the fore questions of home, nation, identity and belonging in narratives where the public and the political are deeply intertwined with the personal. The thesis provides an analysis of the authors’ responses to the events that impacted their lives, their use of English, and their dynamic process of identity construction, as manifested in their narratives. Recognizing the growing circulation of life writing by Iranian women in the United States, the study also investigates how these life writing deconstruct and/or reinforce Western stereotypes about Iranian women.
I argue that the authors successfully cross borders and boundaries by adopting the life writing genre, an interdisciplinary feminist medium, in their quest to share knowledge about their experiences in their home country, Iran, as well as their lives in Diaspora. And by doing so, it is an act of transnationalization of their narratives and a call for human as well as women’s rights. I also assert that their life writing not only reflect the feminist slogan the personal is political but show as well that the political is personal in the life of these women authors who probe the region’s history and its geopolitical trajectory.
Furthermore, the study confirms that these life writing came in waves in response to socio-political as well as historical events in Iran and the host country such as, the Islamic Revolution (1979), the 9/11 tragedy (2001), the Iraq War (2003), and the 2009 Iranian presidential elections. Moreover, the findings show that these life writing articulate what Homi Bhabha refers to as “hybrid” and “ambivalent” identities. The women authors—living in what Brah (1996) calls a “diaspora space” —carve complex, multifold identities that draw upon Iranian cultural values and practices, as well as those of the host countries. They also construct hybridized spaces—that defy strict categorization—both for themselves as well as other women in the diaspora.


identifier: Ahmad Khan Beigi, Nazli  (2020) Iterative Joint Detection in Coordinated Network Coding for Next Generation of Communication Systems.  PhD thesis, Concordia University.

abstract:
The ubiquitous wireless networks in the next generation of communication systems have motivated advanced techniques with diverse ranges of connectivity, coverage, reliability, and throughput. The massive connectivity in the context of the heterogenous networks has conveyed to different sorts of challenges including inter-cell and intra-cell originated interferences. The complications aggravate due to the sporadic nature of the traffic generated by large-scale and low-powered networks over limited spectrum resources.
In this thesis, different techniques in enhancing the reliability, as well as spectral and power efficiency in the future generations of the multi-point communication networks have been investigated. Our proposed schemes are based on the coordination of the transmitters in sharing the source information followed by the joint transmission and the iterative detection. in the context of the cooperative source and channel coding. The Non-Orthogonal Multiple Access (NOMA), Coordinated Multi-Point (CoMP) transmission and the Iterative Joint Detection and Decoding (IJDD) receivers are the frameworks that we have used to validate our proposed improvements.
We have initially investigated the cooperative NOMA as the physical layer network coding scheme in the downlink of wireless communication systems. It is proposed to benefit from the so-called interference received from adjacent cells instead of ignoring or cancelling them, as in the state-of-the-art systems. The application of cooperative NOMA is evaluated in a system-level information theoretic framework to optimize the user-pairing strategy. The results show the cell edges with the strongest interference are the optimal vicinity for the NOMA applications.
Further, we have evaluated the NOMA for the uplink in the dense Internet of Things (IoT) systems, where the sensor elements observe the correlated sources. Realizing that the separation of source coding from channel coding in NOMA systems with correlated sources is suboptimal, we propose our scheme based on the cooperative source and channel coding. The transmitters are assumed to be privy to the whole data through a high-rate and low-latency background connection. The cooperative source coding is then followed by the transmission over the non-orthogonal multiple access (NOMA) channel.
As the transmit signals experience different delay-spreads through the channel, the data streams are received asynchronously, resulting in inter-symbol interference (ISI) at the receiver. We show that the correlated nature of the asynchronous channels can be exploited as the extra source of information, provided that a proper detection technique is adopted. The capacity region is developed, where the sum-rate exceeds that of the synchronous NOMA. The potency of the successive interference cancellation (SIC) receivers, as the main block in NOMA receivers, is investigated. By applying water-filling and geometric power allocation, we show that the NOMA performance degradation in asynchronous channels is caused by the nature of SIC. We have proposed our iterative joint detection and decoding (IJDD) receiver that outperforms SIC in asynchronous NOMA receivers.
Moreover, we have addressed two key challenges in Coordinated Multi-point (CoMP) networks. The asynchronous downlink and imperfect channel state information (CSI) are jointly considered in an information theoretical framework. We assume delays from the Transmission and Reception Points (TRP) to the target user, in general, may exceed cyclic prefix (CP) length, causing symbol-asynchronous reception at the receiver. We characterize an accurate mathematical model for the asynchronous Rayleigh fading channel with imperfect CSI for multi-TRP schemes. We have derived the capacity region for asynchronous CoMP systems and have generalized it to the multi-TRP schemes. We propose a low-complexity iterative detection scheme targeting minimizing the mean square error (MMSE) in our asynchronous fading channel model.
Finally, we have associated the coordinated multi-point transmission with NOMA methodology. We have considered the downlink CoMP in a Single-Frequency Network (SFN) of Digital Terrestrial Television (DTT) broadcasting network. The coordinated transmit signals are assumed to have embedded Layered-Division Multiplexing (LDM) to enhance the coverage, reliability, and spectral efficiency in multi-content broadcasting. We have extended the MMSE-IJDD receiver to higher order modulation formats and have evaluated the order of the computational complexity for our proposed receiver to be in a decent range.
Our extensive simulations validate the proposed scheme providing a considerable boost in the channel reliability, while enhancing the spectral and power efficiency, even as the number of TRPs increases.


identifier: Ouhnana, Sophia  (2020) The Healer’s Paradox: An ethnographic study with Berber Saints in the Moroccan Mid-Atlas.  Masters thesis, Concordia University.

abstract: The Beni M’tir Berber tribe of Ait Ouefella are thought of as direct descendants of a prophetic lineage, possessing a special title in Moroccan society known as chorfa. The chorfa are believed to possess an innate sanctity or holiness manifesting through curative abilities. The existence of such healers is confirmed as an extension of Northwestern African saint veneration (maraboutism); a syncretic, dissonant yet surviving trace of indigenous beliefs otherwise absent in the rest of the Arabized world. The nation’s anthem is the ‘Cherifian Anthem.’ The Royalty are chorfa. Chorfa is thus regarded as a central organizing animus of the North African kingdom in this ethnographic work. To understand the chorfa, one must become one, if possible. Accordingly, a multi-sited fieldwork in Morocco, with apprenticeship as a principal research methodology, was undertaken over the summer of 2019. The objectives were to comprehend the chorfa’s place in society, its evolving relationship with the land and within the Arabized Berber saint paradox. The legend and historiography of the chorfa are explored according to the Mid- Atlas saints’ own narratives. This thesis features oral histories, tales of healing, chorfic curative methods, roles, inheritance mechanisms and their ironic tensions. Healing emerges as a product of conditioning and expectancy processes engendering placebo-like effects. The place of healing is examined with such intimacy that the inverted cursing abilities of the chorfa are revealed. Healing is situated within its larger socio-economic context delving into important questions of public health, social change, environmental preservation, anarchy, morality and indigenous resilience.