identifier: Su, Chang  (2020) Heart Rate Variability Feature Selection using Random Forest for Mental Stress Quantification.  Masters thesis, Concordia University.

abstract:
Mental stress is considered as an essential element that affects decision making. Apart from mental stress, cognitive workload, mental effort, attention, and cognitive engagement are also involved in the decision-making process. Ambiguities of these concepts lead to confusion in their applications.
One objective of this thesis is to explore the relationship between mental stress and stress-related concepts. By investigating the mechanisms for decision-making, the difference and correlation of mental stress and other concepts are disclosed.
Heart rate variability (HRV) is a common method to measure mental stress. By investigating the correlation between HRV and mental stress, it can be confirmed that HRV does respond to mental stress changes instead of other concepts. HRV features are used to assess whether there is a relationship between baseline HRV and mental stress. However, the extracted features usually contain a large amount of redundancy, which adds computational complexity to mental stress quantification while not contributing to quantification accuracy. Recently, researchers have resorted to the random forest as a tool for HRV feature selection.
Another objective of this thesis is to select significant HRV features to quantify the mental stresses using the random forest method.
In this thesis, an open-source data set, called the SWELL-KW data set, is used for mental stress measurement, where three labels are assigned according to different mental stress conditions, i.e., neutral, time pressure, and interruption. A set of HRV features are proposed based on time domain and frequency domain analysis for mental stress measurement. Statistical analysis is performed to select the essential features that reflect mental stress.
The random forest algorithm of feature selection is then studied, and the accuracy in measuring mental stress is validated by comparing the extracted features of the training set and the testing set. In order to evaluate the random forest algorithm's performance, the comparisons with other related algorithms, including support vector machine (SVM), decision tree, gradient boosting decision tree (GBDT), k-nearest neighbor algorithm (KNN), and deep neural networks (DNN), are also conducted in terms of accuracy and time cost.
The optimal HRV feature subset is proposed for mental stress quantification, including median RR, mean RR, median REL RR, HR, pNN25, SDRR RMSSD, SDRR RMSSD REL RR, TP, SD2, and SDRR. It is shown that this subset of features gives a high feature importance score and thus has a significant effect on mental stress quantification.
Performing random forest analysis with a sufficient amount of labeled data shows that the optimal HRV feature subset yields high mental stress quantification accuracy by using random forest. Moreover, random forest always makes the best overall performance in feature selection compared with other algorithms in terms of accuracy and time cost. It also infers the potential relation between physiological responses and mental activities.


identifier: Sakib, Faruk Ahmed  (2020) Wind load on canopies attached to buildings of different heights.  Masters thesis, Concordia University.

type: Thesis NonPeerReviewed

abstract:
Residential and industrial buildings commonly have various types of overhangs attached to their walls for the conveniences of their users. These overhangs have different names like canopy, patio cover, porch etc. Depending on their locations, canopies are very prone to wind due to the suction developing on their upper surface along with the pressure occurring on their lower surface (for most wind directions), which together may generate critical uplift forces causing lots of damage on these non-structural elements. Very limited studies have been carried out on wind loading on attached canopies. Current ASCE 7-16 provides a procedure for calculation of wind loading on attached canopies. These provisions include a chart to find out both upward and downward wind pressures on the attached canopy. It should be noted that ASCE provisions have limitations and are only applicable for buildings up to 60 ft high. Past and recent studies on this topic have also been limited to low-rise buildings only. Thus, structural engineers have long been asking for guidance for estimation of wind loads that may act on canopies in tall buildings. In addition, the effect of canopy width has not been investigated thoroughly.
This thesis presents a study on the effect of wind loading on attached canopies in tall buildings. In this study, high-rise (37 meters) buildings were tested with canopy attached to the wall at different heights. Canopy with different widths were also tested. In addition to the high-rise building (37 m), canopies attached to two other building heights (18.5 m and 7 m) were considered. The test program, which was carried out in the Wind Tunnel Laboratory of Concordia University, Montreal, shows that canopy attached at the top of a tall building may experience 70% more suction than that of a low-rise building. In addition, this thesis also presents the effect of building height, canopy height, wind angle of attack and effect of considered area on wind loading on canopy, which will help structural engineers better understand the behavior of canopies under wind loads both in low-rise and taller buildings. Design provisions for appropriate wind forces for canopies in taller buildings are also provided at the end of this thesis to help the structural engineers


identifier: Barendrecht, Casper  (2020) Shafarevich-Tate groups for some Modular Abelian Varieties.  Masters thesis, Concordia University.

abstract:
Let f be a weight 2 newform of level N, and let A be the associated modular abelian variety.
Let Kbe an imaginary quadratic field of discriminant D different from -3 and -4, and let p be a prime of the endomorphism ring of A outside a finite set S.
If A admits a principal polarization, and the Heegner point associated to K has infinite order in A(K), then the Shafarevich-Tate group is finite and its p-primary part is a perfect square.
Generalizing the work of Kolyvagin and McCallum, we give an explicit structure of the p-primary part of the Shafarevich-Tate group.

This thesis aims to provide an accessible proof of this statement for those with restricted knowledge on the subject.
The first three chapters offer an introduction to the basic notion of arithmetic geometry. Chapters 4 and 5 expand on the theory spefic to the thesis. Finally chapter 6 combines the developed theory to proof this structure theorem for Shafarevich-Tate groups.


identifier: Kamgnia Wonkap, Stephanie  (2020) Gene Regulatory Network Inference Using Machine Learning Techniques.  PhD thesis, Concordia University.

abstract:
Systems Biology is a field that models complex biological systems in order to better understand the working of cells and organisms. One of the systems modeled is the gene regulatory network that plays the critical role of controlling an organism's response to changes in its environment. Ideally, we would like a model of the complete gene regulatory network. In recent years, several advances in technology have permitted the collection of an unprecedented amount and variety of data such as genomes, gene expression data, time-series data, and perturbation data. This has stimulated research into computational methods that reconstruct, or infer, models of the gene regulatory network from the data. Many solutions have been proposed, yet there remain open challenges in utilising the range of available data as it is inherently noisy, and must be integrated by the inference techniques. The thesis seeks to contribute to this discourse by investigating challenges of performance, scale, and data integration.

We propose a new algorithm BENIN that views network inference as feature selection to address issues of scale, that uses elastic net regression for improved performance, and adapts elastic net to integrate different types of biological data. The BENIN algorithm is benchmarked on a synthetic dataset from the DREAM4 challenge, and on real expression data for the human HeLa cell cycle. On the DREAM4 dataset BENIN out-performed all DREAM4 competitors on the size 100 subchallenge, and is also competitive with more recent state-of-the-art methods. Moreover, on the HeLa cell cycle data, BENIN could infer known regulatory interactions and propose new interactions that warrant further experimental investigation.

Keys words: gene regulatory network, network inference, feature selection, elastic net regression.


identifier: Alalyan, Fahdah Abdullah  (2020) Statistical Approaches for Binary and Categorical Data Modeling.  Masters thesis, Concordia University.

abstract:
Nowadays a massive amount of data is generated as the development of technology and services has accelerated. Therefore, the demand for data clustering in order to gain knowledge has increased in many sectors such as medical sciences, risk assessment and product sales. Moreover, binary data has been widely used in various applications including market basket data and text documents analysis. While applying classic widely used k-means method is inappropriate to cluster binary data, we propose an improvement of K-medoids algorithm using binary similarity measures instead of Euclidean distance which is generally deployed in clustering algorithms. In addition to K-medoids clustering method, agglomerative hierarchical clustering methods based on Gaussian probability models have recently shown to be efficient in different applications. However, the emerging of pattern recognition applications where the features are binary or integer-valued demand extending research efforts to such data types. We propose a hierarchical clustering framework for clustering categorical data based on Multinomial and Bernoulli mixture models. We have compared two widely used density-based distances, namely; Bhattacharyya and Kullback-Leibler. The merits of our proposed clustering frameworks have been shown through extensive experiments on clustering text, binary images categorization and images categorization.

The development of generative/discriminative approaches for classifying different kinds of data has attracted scholars’ attention. Considering the strengths and weaknesses of both approaches, several hybrid learning approaches which combined the desirable properties of both have been developed. Our contribution is to combine Support Vector Machines (SVMs) and Bernoulli mixture model in order to classify binary data. We propose using Bernoulli mixture model for generating probabilistic kernels for SVM based on information divergence. These kernels make intelligent use of unlabeled binary data to achieve good data discrimination. We evaluate the proposed hybrid learning approach by classifying binary and texture images.


identifier: Valentine Freeman, Jeremy  (2020) Un-Decadence: Counter-Poetries and Decadent Aesthetics in the British Fin-De-Siècle.  PhD thesis, Concordia University.

abstract: This dissertation examines and categorically reforms the accepted construction of fin-de-siècle decadence. In this study I grapple with how we understand the nature of fin-de-siècle cultural history and how theories of decadence during the late-nineteenth century may be usefully complicated by an analysis of fin-de-siècle decadence as a non-unitary discursive program. Cumulatively this study reimagines Victorian, fin-de-siècle and modernist literatures in relation to decadent aesthetics read through a sustained investigation of discourse and generic forms in the period. To this end I examine three late-Victorian poets’ methods of construction and models of poetic and aesthetic form as they wrote, shaped and published their works in order to demonstrate how they incite us to alter and re-define our conception of decadence as a historical category. For this critical intervention I have chosen a set of authors—Edward Carpenter, John Davidson and Sarojini Naidu—whose work exemplifies three poetic models that, I argue, actively engaged with and reformed the tropes, arguments and methods of fin-de-siècle decadence. Edward Carpenter’s poetry is emblematic of democratic decadence and exemplifies a homoerotic, vegetarian, fraternal, and socialistic decadence that combines late-century aestheticism and utopianism with the embrace of a cosmic Whitmanian self and an ardent embrace of Indian spirituality and culture. John Davidson’s poetics are a case-study in muscular decadence with its roots in Nietzsche, the Nietzschean will to power and in a cross-fertilization of German and European pessimism with particularly British ideas of national culture and the great self. Sarojini Naidu’s poetics are a case-study in poly-decadence, a poly-cultural, poly-theistic, and poly-valent poetics that explores the international shape of decadent symbolism and a wide movement toward an emancipatory decadence intertwined with a nationalistic feminism. By organizing and naming the key decadent aesthetic fields that the authors engaged with this study traces the dialectical interplay between poetry and decadent theory during the period and attempts to broaden and complicate how we understand decadence, discourse and aesthetics as key drivers in the production of culture.


identifier: Tousignant, Brigitte  (2020) A Hybrid Analysis of the State of Automated Journalism in Canada: Current Impact and Future Implications for Journalists and Newsrooms.  Masters thesis, Concordia University.

abstract:
In recent years, certain media organizations around the world have begun adopting automated journalism to enhance their newsrooms’ productivity and reporting capacity. These tools, which can be fully- or semi-autonomous expert systems, are being implemented for a multitude of reasons, such as to ease journalists’ workflow, to expand news coverage, to uncover complex investigations and to cut costs. While some have argued automation can help increase newsroom efficiency and output, questions regarding authorship and transparency, and the implications for journalists, have come into question. Despite automated journalism’s growing presence, little is known about its integration in and influence on the Canadian news media landscape.
	This thesis reports on the results of qualitative interviews with nine journalists and news media professionals and aims to examine how automated journalism techniques and tools are being integrated, and the effects they have on journalists, their practices, methods, and what is being demanded of them. The purpose of this thesis is to fill a gap in the literature and in our knowledge about automated journalism’s role in Canada. Its findings conclude that, although Canada is behind in the adoption of automated journalism, there is an overwhelming consensus that the ethical guidelines of the Canadian news media industry need to be revised in order to better frame the use of automated technologies.


identifier: Almakhadmeh, Mohammad  (2020) ALKALI ACTIVATED MATERIALS: INTERACTIONS BETWEEN INGREDIENTS TO ACHIEVE SUSTAINABILITY.  Masters thesis, Concordia University.

abstract: Extensive researches were conducted on alkali-activated materials (AAM) as a green sustainable alternative for ordinary Portland cement (OPC). Recently, there was a shift to one-part alkali-activated materials to avoid the use of corrosive activator and also to reduce the carbon footprint. Most of the researches had focused on these powder activators types and impacts on the final product properties. However, limited research had investigated the interaction of these activators and other ingredients. Hence, the novelty of this work stems from studying these interactions and illustrating their effect on the developed One-Part AAM properties. Mixing water is a critical parameter in controlling the fresh and hardened properties of construction materials. Hence, the interaction between mixing water and the anhydrous activator was examined. Also, water effluents from various industrial recourses were tested. The study highlighted, for the first time, the role of water temperature in controlling the dissolution rate of solid activators. It emphasizes how this interaction can significantly change the fresh and hardened properties. On the other hand, superplasticizer and other chemical admixtures are commonly added to enhance cementitious materials flowability. However, the high alkalinity of AAM had drastically affected their effectiveness. Hence, a premixing technique was tested to produce AAM with high flowability. Various wastes, namely, red Mud, paper mill sludge, and paper mill fly ash, were examined as a potential precursor for AAM. The study showed the potential for developing a new smart, sustainable construction material/product incorporating such industrial wastes while maintaining adequate mechanical and durability performance. It is anticipated that the findings of this study will i) lead to saving energy/reducing fossil fuel consumption to heat water during cold weather concreting will increase the sustainability level of the construction sector, ii) replace or reduce the demand for admixtures leading to both economic and environmental benefits, and iii) reduce the amount of industrial wastes sent to reservoirs through recycling/reusing in various construction applications leading to social and environmental benefits. Also, the outcomes of this research will allow the industrial sector to economically and sustainably transforming wastes into a high-value product.


identifier: Hoteit, Rasha  (2020) A Story of Becoming an Artist/Teacher.  Masters thesis, Concordia University.

title: A Story of Becoming an Artist/Teacher

abstract: “We teach who we are,” says Parker Palmer. Building on his statement and guided by a social constructivist perspective, Becoming an Artist/Teacher is a semi-autobiography that tells my story with art education. In parallel, it tells the stories of two other art educators in Lebanon by looking at the context and the relational and institutional experiences that molded our lives. By weaving together two biographical narratives and one autobiography that share similar sociopolitical, economic, and educational experiences, this research looks at how each participant built their unique professional identity. How did life events and relationships influence their teaching philosophies and art practices? And how did their different understandings of a teacher’s identity influence their relationships with spaces and people as well as their own personal experiences? Based on interviews and analysis that followed the Biographic Narrative Interpretive Method (BNIM) and by comparing participants’ life stories, the research has shown that despite not receiving any formal teacher training, the participants were authentic educators. Their dedication, artistic practices, and closeness to the subject of teaching permeated their style, allowing for an authentic teaching approach to emerge. The presentation of the impact of the teachers’ personal understanding of their professional identity and personality on their educational approach, makes the case for the importance of specialization and highlights the need to include self-study as a fundamental part in the formation of teachers in general and art educators in particular.


identifier: Hosseini Rahdar, Mohammad ORCID: https://orcid.org/0000-0002-0293-2296 <https://orcid.org/0000-0002-0293-2296>  (2020) A Bayesian Approach to The Assessment of Fuel Composition Variability Effects on Grate-bed Biomass Combustion.  PhD thesis, Concordia University.

abstract:
Combustion systems are the most energy-intensive facilities in the world. They are responsible for releasing the majority of the greenhouse gases (GHG) and NOx into the earth’s atmosphere. Biomass is the only renewable energy source consisting of fixed carbon elements which can be substituted for fossil fuels in combustion systems. The main distinction between biomass and fossil fuel combustion is fewer pollutant emissions of biomass combustion, as well as, biomass combustion’s lower price and simpler storage facility. So far, direct combustion of the solid biomass is the most popular method, both thermally and economically, among all various bioenergy systems, which is due to the price of biofuels process cost. Grate firing technology is of interest to burn solid biomass because it has less sensitivity to feed composition and size, which shows the excellent potential of this technology. However, owing to the intrinsic composition variability of biomass, there are still uncontrolled deflections associated with biomass combustors operations.
This study is an effort to quantify the overall impact of fuel compositions variability on moving bed biomass combustion, which will facilitate the understanding of biomass combustion. Randomly selected biomass pellets were individually investigated via a Thermogravimetric Analysis (TGA) to specify the fuel compositions; moisture, volatile, char, and ash. This data, together with the predefined fuel composition provided by fuel supplier are utilized to train a model using a Bayesian approach to populate our measured data. Simultaneously, a 1D transient numerical model of moving bed biomass combustion is deliberately developed corresponding to the research goals. The model iteratively runs with distributed fuel composition made by the Bayesian data generator and simulates the combustor under uncertain conditions. The comprehensive thermo-economic and environmental analysis of the biomass boiler operated with the three most common biomass types was conducted. Specifically, this includes biomass pellets, wood waste, and municipal solid waste and through this research showed that biomass pellets are the most efficient in terms of thermal operation and financial revenue. An experiment-based approach to the composition uncertainty impact of biomass pellets and bamboo chips on moving bed combustors were also practiced. While a notable heat flux deviation from mean operation conditions was observed for both, the pelletizing helped pellets to limit the level of uncertainty to a satisfying degree. Higher char content can limit the combustion uncertainty to a strong extent, while the moisture content was found to be the main contributor to the level of uncertainty. As well, NOx emission arising from biomass combustion fluctuated up to 17% due to composition variability. Finally, combustor operations under more reliable input data via the Bayesian data generator showed a remarkable system deviation from that of predefined input conditions. Overlooking the fuel compositions variability caused an overestimation of heat generation of up to 8.5%. Moreover, a notable amount of unburned biomass particles was sent to an ash bin, which is not in line with biomass harvesting sustainability. To avoid this in the future, the system must be regulated to correspond to the fuel compositions offered by the Bayesian model.


identifier: Handa, Gaurav  (2020) A Pipeline for Modelling of Ice-Hockey Stick Shape Deformation Using Actual Shot Video.  Masters thesis, Concordia University.

abstract:
In Ice-Hockey, performance of the player’s shots depends on their skill level, body strength as
well as stick’s construction and stiffness. In fact, research suggests that one of the primary reasons
that the elite players generate much faster shots is their ability to flex their hockey stick. Thus,
reconstructing the deformable 3D shape of the stick during the course of a player shot has important
applications in performance analysis of ice-hockey stick.
We present a new, low cost, portable system to acquire videos of a player shot and to automatically reconstruct the stick shape’s deformation in 3D. This thesis is a sub-part and contributes
towards the ultimate goal of the pipeline in many different ways. First, designing a mobile stereovision setup and its calibration, capturing a lot of data acquisitions with different players shooting
in different styles. Second, developing a two step pruning methodology to prune structurally thin
and fast moving ice-hockey stick from noisy reconstructed point cloud in 3D. Third, automating the
process of initial rigid alignment of the stick template in the noisy reconstruction. Forth, reducing
the effect of noise by using medial axis approximation approach and suppressing the hand occlusion
effect on the final template bending by a curve fitting approach. This pipeline is also robust against
different ice-hockey sticks along with different players, shooting at different styles.


identifier: Lefebvre, Melanie  (2020) Centering Stories by Urban Indigiqueers/Trans/Two-Spirit and Indigenous Women on Practices of Decolonization, Collective-care and Self-care.  Masters thesis, Concordia University.

format: text audio audio audio audio audio audio audio audio audio audio

abstract: Centering the voices of Indigiqueers, Trans, Two-Spirit (2S) people and Indigenous women shines light where we find radical thought, grassroots action and rebellious forms of care. This revolutionary practice ultimately disturbs the systems of colonialism and heteronormativity that work to oppress QTBIPOC (Queer, Trans, Black, Indigenous, People of Color) communities. The gendered processes of colonialism in so-called North America have disrupted the balance of gender roles and leadership responsibilities/capacities traditionally held by non-binary, gender fluid and Trans folks in our communities. Government legislation and policy such as the Indian Act, the reserve system and residential schools have led to social and economic disparity for Indigenous peoples and forced diaspora to urban centres, where Indigenous community is now comprised of a variety of nations coming together in solidarity to share and support one another. Edited into nine podcast episodes titled kiyanâw maskihkîwakan : Our Medicines, this research-creation project centres storytelling by urban Indigiqueers, Trans, Two-Spirit people and Indigenous women on their life journeys and decolonization, collective care and self-care. Storytellers touch on experiences related to identity and belonging, (re)connection, trauma, cultural teachings, creativity, gender and sexuality, body sovereignty, role in community, and notions of and relationships with land. They reveal how we kindle and keep alight kinship relations with each other and ourselves on the land – whether urban, rural or cyber scapes – as we navigate these settled spaces toward possible Indigenous futures.


identifier: Tasneem, Tamanna  (2020) Venture Capital Funding After Crowdfunding Success: A Study of Successful Kickstarter Campaigns.  Masters thesis, Concordia University.

abstract: For start-ups, a successful crowdfunding campaign could generate significant opportunities to access additional funding resources. This paper contributes to advancing our understanding of how characteristics of a successful Kickstarter campaign may determine a start-up’s follow-on fundraising performance, with a special focus on future venture capital (VC) financing. We draw on 483 successfully crowdfunded projects on Kickstarter and more than 80 follow-on VC investments throughout the period 2010-2018 to investigate how various venture quality attributes (e.g. high pledged amount, strong social media alliance) and uncertainties (e.g. large number of backers) contribute to the subsequent increase (or decrease) in VC investments. Our results show that larger public investments (i.e. pledge amount) signal greater public confidence, translating into significant rise in the probability and amount of subsequent VC financing. We also find that founders’ professional connections (in LinkedIn) factor in the venture’s future funding success by raising both the probability and number of subsequent VC rounds. Statistically significant results were found supporting our hypothesis that VCs tend to devalue a large crowd of backers, presumably due to future market loss concerns. Both the probability and the number of follow-on VC rounds were negatively affected by a high number of backers. Finally, we show that serial creators with back-to-back crowdfunding successes are more likely to go back to the crowd for future funding needs which translates into higher probability, number and size of subsequent crowdfunding rounds. However this strong association was not found to be negatively affecting the degree of future VC involvement.


identifier: Vatandoost, Hossein ORCID: https://orcid.org/0000-0003-3743-338X <https://orcid.org/0000-0003-3743-338X>  (2020) Compression Mode Characterizations of Magnetorheological Elastomers.  PhD thesis, Concordia University.

abstract:
Magnetorheological (MR) elastomers (MREs) with controllable stiffness and damping properties offer significant potential for fail-safe semi-active and active control of vibration in many engineering applications. While the properties of MREs in the shear mode have been widely characterized, the compression mode properties of the MREs have been addressed in fewer studies. Unlike the shear mode, the compression mode characterization necessitates compensation for the magnetic force developed by the electromagnet in order to extract viscoelastic force from the measured force. In this dissertation research, a systematic methodology and a mathematical model are developed for compensating for the magnetic force for extracting the viscoelastic component of the force attributed to compression mode viscoelasticity of the MRE. For this purpose, an optimal design of a UI-shaped electromagnet was realized with minimum mass and magnetic flux density up to 1 Tesla. An experiment was designed to apply magnetic field in the direction of mechanical loading and to facilitate measurement of magneto-mechanical force. Results revealed peak errors in equivalent stiffness and damping constants of the MRE in the orders of 90% and 163%, respectively, in the absence of magnetic force compensation. The proposed compensation methodology provided a framework for accurate compression mode characterizations of the MREs as functions of the volume fraction and anisotropy of iron particles, and shape factor apart from the magnetic flux density, pre-strain and strain amplitude and strain rate excitations.
MREs typically experience large static pre-strain in many applications in order to support the machine/structure weight, which can alter the distances among the magnetizable particles and thus the MR effect. The dynamic compression mode properties of isotropic and anisotropic MREs with 30% volume fraction of iron-particles and nominal shape factor (SF) of 0.56 were experimentally characterized under broad ranges of strain amplitude (2.5–20%) superimposed on a large static pre-strain of 21%, excitation frequency (0.1–50 Hz) and magnetic flux density (0–750 mT). Subsequently, the experiments were designed to evaluate the effects of the shape factor (SF), pre-strain and particle volume fraction (PVF) on compression mode properties of the isotropic and anisotropic MREs under broad ranges of excitations and magnetic flux density. The measured data were analyzed to evaluate compression mode properties in terms of stress-strain characteristics, relative MR effect, equivalent stiffness, equivalent damping, and elastic and loss moduli as functions of the design factors (pre-strain, PVF, SF and anisotropy) and operating factors (strain amplitude, strain rate and the magnetic flux density). The results invariably, revealed hysteretic stress-strain characteristics with strongly nonlinear and coupled dependence on the various design and operating factors. Among the design factors considered, the PVF revealed greatest effects on the stiffening and dampening behaviors of the MREs. Increasing the SF and PVF generally resulted in substantially higher MR effect in view of the elastic modulus of the MREs. The relative MR effect, however, decreased with increase in the pre-strain.
The influences of various design and operating factors on the compression mode properties of are thoroughly analyzed and discussed, which would serve as design guidance for the MREs in different engineering applications. Phenomenological models with only a few unknown parameters were further formulated to predict the SF-, pre-strain-, and PVF-dependent compression elastic and loss moduli of both types of MREs. The effectiveness of the models is demonstrated by comparing the model-predicted properties with the measured data over broad ranges of design and operating conditions.


identifier: Khanna, Ujjwal  (2020) Computer Vision and Internet of Things Application to Enhance Pedestrian Safety.  Masters thesis, Concordia University.

abstract:
With the increasing population, the issue of pedestrian safety is currently of major concern in
most cities of the world. Pedestrian safety is concerned with ensuring the well-being of pedestrians
and reducing the potential risk areas as well as implementing measures to reduce accidents. The
aim of this study is to propose a computer vision and cloud-based solution that enhances pedestrian
safety by collecting, visualizing and analyzing pedestrian and vehicular data across different
intersections in the city of Montreal. In the past, the rate of accidents in the City of Montreal
involving pedestrians has been quite high, therefore a method to solve this problem has led to this
study.
About 200,000 images were collected across 43 intersections in the city of Montreal from the
Traffic cameras – Ville de Montreal website. The data was collected from March 8, 2020, up until
March 22, 2020 and then from May 1st, 2020 to 11th May 2020. An object detection and
classification model using Faster RCNN algorithm to identify pedestrian and vehicles at the
intersection was implemented. Further, this model was used to obtain a dataset showing the
number of pedestrians and vehicles at the intersections. The information obtained from this data
set was used for visualization and in-depth analysis of the pedestrian and vehicle data in order to
derive patterns of peak and non-peak hours and high-risk intersections.
IV
Furthermore, zero inflation poisson distribution model was implemented on our dataset to
display the timings and intersections which had zero pedestrian counts for long hours of the day
as compared to the vehicle count. A heat map was generated to visualize the dataset and to assist
data viewers to identify which areas should get most attention.
Finally, we created a prototype solution that mimicked the traffic control system by utilizing
LEDs and microcontrollers (IoT device), cloud services, publish/subscribe model, and object
detection. To implement this prototype, the data obtained through the object detection model was
sent onto the cloud (Cloud MQTT), from where it was used to control the programmed
microcontrollers (IoT devices) present at the different intersections based on the vehicle and
pedestrian counts. The system managed to show excellent accuracy for detection of vehicles and
pedestrians on the dataset, and the delay experienced in controlling the microcontroller was also
negligible, thus making our system effective and reliable.


identifier: Deshpande, Prerna, Saadawi, Nour, Stojanovic, Jovana, Ribeiro, Paula and Bacon, Simon L.  (2020) Sex-specific response to physical activity changes using e-health behaviour change interventions: a systematic review and Meta Analysis.  Masters thesis, Concordia University.

creator:
Deshpande, Prerna
Saadawi, Nour
Stojanovic, Jovana
Ribeiro, Paula
Bacon, Simon L.

abstract:
Background: Non-communicable diseases (NCDs) account for 70% of deaths globally every year. Cancer, diabetes, cardiovascular diseases, and chronic pulmonary diseases are four commonly caused NCDs. These NCDs usually occur due to modifiable risk factors such as poor diet, alcohol intake, and physical inactivity. Physical activity (PA) has benefits on reducing NCDs and the overall wellbeing of adults. E-health behaviour change interventions with PA outcomes, even though aimed at helping adults increase their PA, may differ in responses between men and women.
Objective: The aim of this systematic review was to synthesise the current evidence on sex-differences in PA changes after an e-health behaviour change intervention.
Methods: Electronic databases PubMed, Web of Science, PsycInfo, and Cochrane were searched to retrieve papers published in peer-reviewed journals. Articles were included, that provided descriptions of interventions that employed e-health delivery modes such as Internet, email, and short messaging services (SMS), included PA as one of their outcomes and focused on healthy adult populations.
Results: In total 18 studies were included in the current systematic review. Of the 18 studies, 14 studies were analysed in both meta-analysis and qualitative synthesis. These 14 studies measured PA subjectively and had usable outcome data. In all, the group comparison showed no significant sex-effect (p=0.685) on changes in PA, post-intervention, whereas comparisons of pooled effects
iv
of men and women at follow-up showed a significant effect (p=0.008) indicating greater increases in PA among women over the long-term (mean follow-up = 23.5, SD = 12.7 weeks).
Conclusions: E-health behaviour change interventions lead to significant increases in PA, at both post-intervention and follow-up among men and women, with greater increases at follow-up among women compared to men. Clinically, it means that both men and women maybe able to maintain sustained increase in PA using e-health behaviour change interventions.


identifier: Ibrahim, Zinatu  (2020) Interest rate models for direct participation products under IFRS 17.  Masters thesis, Concordia University.

abstract: The incoming IFRS 17 standard will require insurers to adopt market-consistent methods in valuing their insurance contract liabilities. Insurance contracts such as segregated funds categorized as direct participation contracts are highly dependent on the choice of interest rate model. This thesis describes the general steps in the calibrating the G2++ and G3++ short rate models to swaptions. The calibration process is an optimization problem where parameters that minimize the difference between market and model implied prices are determined. We highlight some issues that arise during the calibration such as the choice of a calibration method and an optimization algorithm as well the imposed constraints used in solving this inherent non-convex global optimization problem.  An evaluation of the calibrated models' consistency to replicate market volatility surface is made via Monte-Carlo simulations. Then a simulation scheme devoid of discretization errors is provided for the stochastic discount factor, the short rate and bond prices which are useful in market-consistent valuations. We provide an illustration of a market-consistent valuation of a segregated fund fully invested bonds and compare the models' results. Finally, we assess the robustness of the models' calibration to different market data and parameter specification. This is to ensure that the calibrated models provide stable financial results for annual regulatory reporting.


identifier: Wu, Xinyi  (2020) Robust Position-based Visual Servoing of Industrial Robots.  Masters thesis, Concordia University.

abstract:
Recently, the researchers have tried to use dynamic pose correction methods to improve the accuracy of industrial robots. The application of dynamic path tracking aims at adjusting the end-effector’s pose by using a photogrammetry sensor and eye-to-hand PBVS scheme. In this study, the research aims to enhance the accuracy of industrial robot by designing a chattering-free digital sliding mode controller integrated with a novel adaptive robust Kalman filter (ARKF) validated on Puma 560 model on simulation. This study includes Gaussian noise generation, pose estimation, design of adaptive robust Kalman filter, and design of chattering-free sliding mode controller. The designed control strategy has been validated and compared with other control strategies in Matlab 2018a Simulink on a 64bits PC computer. The main contributions of the research work are summarized as follows.

First, the noise removal in the pose estimation is carried out by the novel ARKF. The proposed ARKF deals with experimental noise generated from photogrammetry observation sensor C-track 780. It exploits the advantages of adaptive estimation method for states noise covariance (Q), least square identification for measurement noise covariance (R) and a robust mechanism for state variables error covariance (P). The Gaussian noise generation is based on the collected data from the C-track when the robot is in a stationary status. A novel method for estimating covariance matrix R considering both effects of the velocity and pose is suggested.

Next, a robust PBVS approach for industrial robots based on fast discrete sliding mode controller (FDSMC) and ARKF is proposed. The FDSMC takes advantage of a nonlinear reaching law which results in faster and more accurate trajectory tracking compared to standard DSMC. Substituting the switching function with a continuous nonlinear reaching law leads to a continuous output and thus eliminating the chattering. Additionally, the sliding surface dynamics is considered to be a nonlinear one, which results in increasing the convergence speed and accuracy.

Finally, the analysis techniques related to various types of sliding mode controller have been used for comparison. Also, the kinematic and dynamic models with revolutionary joints for Puma 560 are built for simulation validation. Based on the computed indicators results, it is proven that after tuning the parameters of designed controller, the chattering-free FDSMC integrated with ARKF can essentially reduce the effect of uncertainties  on robot dynamic model and improve the tracking accuracy of the 6 degree-of-freedom (DOF) robot.


identifier: Moslehi, Parisa  (2020) Mining and linking crowd-based software engineering how-to screencasts.  PhD thesis, Concordia University.

abstract:
In recent years, crowd-based content in the form of screencast videos has gained in popularity among software engineers. Screencasts are viewed and created for different purposes, such as a learning aid, being part of a software project’s documentation, or as a general knowledge sharing resource. For organizations to remain competitive in attracting and retaining their workforce, they must adapt to these technological and social changes in software engineering practices.
In this thesis, we propose a novel methodology for mining and integrating crowd-based multi- media content in existing workflows to help provide software engineers of different levels of experience and roles access to a documentation they are familiar with or prefer. As a result, we first aim to gain insights on how a user’s background and the task to be performed influence the use of certain documentation media. We focus on tutorial screencasts to identify their important information sources and provide insights on their usage, advantages, and disadvantages from a practitioner’s perspective. To that end, we conduct a survey of software engineers. We discuss how software engineers benefit from screencasts as well as challenges they face in using screencasts as project documentation.
Our survey results revealed that screencasts and question and answers sites are among the most popular crowd-based information sources used by software engineers. Also, the level of experience and the role or reason for resorting to a documentation source affects the types of documentation used by software engineers. The results of our survey support our motivation in this thesis and show that for screencasts, high quality content and a narrator are very important components for users.
Unfortunately, the binary format of videos makes analyzing video content difficult. As a result, dissecting and filtering multimedia information based on its relevance to a given project is an inherently difficult task. Therefore, it is necessary to provide automated approaches for mining and linking this crowd-based multimedia documentation to their relevant software artifacts. In this thesis, we apply LDA-based (Latent Dirichlet Allocation) mining approaches that take as input a set of screencast artifacts, such as GUI (Graphical User Interface) text (labels) and spoken words, to perform information extraction and, therefore, increase the availability of both textual and multimedia documentation for various stakeholders of a software product. For example, this allows screencasts to be linked to other software artifacts such as source code to help software developers/maintainers have access to the implementation details of an application feature.
We also present applications of our proposed methodology that include: 1) an LDA-based mining approach that extracts use case scenarios in text format from screencasts, 2) an LDA-based approach that links screencasts to their relevant artifacts (e.g., source code), and 3) a Semantic Web-based approach to establish direct links between vulnerability exploitation screencasts and their relevant vulnerability descriptions in the National Vulnerability Database (NVD) and indirectly link screencasts to their relevant Maven dependencies. To evaluate the applicability of the proposed approach, we report on empirical case studies conducted on existing screencasts that describe different use case scenarios of the WordPress and Firefox open source applications or vulnerability exploitation scenarios.


identifier: Nguyen, Phuoc Huy Nhut  (2018) Earth Pressures of Overconsolidated Collapsible Soil Subjected to Inundation.  PhD thesis, Concordia University.

abstract:
Collapsible soils are known as problematic soils, which can be found in many regions around the world. Collapsible soils possess considerable strength when they are dry; however, when they are inundated, they lose their strength and exhibit excessive settlement. The amount of soil collapse increases with the increase of the so-called collapse potential “Cp”, the wetting zone, and the degree of saturation (S). Accordingly, maximum collapse will take place due to full saturation. Collapsible soils can be inundated by heavy and continuous rainfall, excessive irrigation, broken water/sewer lines, or by rising the ground water. Furthermore, collapsible soils can be also found in construction site, in compacted fine soils at low water content (less than the optimum water content). Consequently, it is impossible to avoid construction on collapsible soils, which are potential for excessive settlement, differential settlement, landslides and falls, earth cracks. This type of soil has been responsible for damaging variety of civil engineering structures, loss of lives. Construction on such kind of soil shows extraordinary geotechnical problems, retaining walls are not an exception. With civilization, backfills behind retaining walls made of collapsible soils are widely used in practice. The earth pressures acting on these walls experience radical changes when the backfills of collapsible soils are wetted.
In the literature, there are lack of theories/methods for estimating the earth pressures acting on walls retaining collapsible soils. In this study, experimental investigations on at-rest and passive earth pressures of overconsolidated collapsible soil on retaining walls were conducted.A prototype model of a vertical wall, retaining horizontal backfill of collapsible soil, was developed in the laboratory. Collapsible soil was prepared in the laboratory by mixing kaolin clay with fine sand. The model was instrumented to measure the earth pressure at strategic points on the wall, the total earth pressure acting on the wall, and the overconsolidation ratio (OCR) of the soil. Tests were conducted on the wall retaining collapsible soil at the dry and at fully saturated conditions for both the at-rest and passive earth pressures. The test results showed that both the at-rest and passive earth pressures increased with the increase of the collapse potential (Cp) and overconsolidation ratio (OCR) for the dry soil. At full saturation, the at-rest and passive earth pressures reduced considerably. Generally, the higher the collapse potential of the collapsible soil, the larger the decrease in the at-rest and passive earth pressures of the soil when the soil gets inundated.
In this investigation, for the case of at-rest earth pressure, empirical formulae were developed to determine the coefficient of at-rest earth pressure (K0) of the dry and saturated overconsolidated collapsible soil. For the case of passive earth pressure, analytical model was developed. Accordingly, design theory was presented for estimating the coefficient of passive earth pressure (Kp) of overconsolidated collapsible soil at the dry and saturated conditions. Moreover, considering for the case of presence of unsaturated states of collapsible soil in practice, design charts were developed to assist designers in approximating the coefficient of at-rest (K0) and passive (Kp) earth pressures of this soil at different degree of saturation.


identifier: Khazaee, Saeed  (2020) Detection of counterfeit coins based on 3D Height-Map Image Analysis.  PhD thesis, Concordia University.

abstract:
Analyzing 3-D height-map images leads to the discovery of a new set of features that cannot be extracted or even seen in 2-D images. To the best of our knowledge, there was no research in the literature analyzing height-map images to detect counterfeit coins or to classify coins. The main goal of this thesis is to propose a new comprehensive method for analyzing 3D height-map images to detect counterfeit of any type of coins regardless of their country of origin, language, shape, and quality. Therefore, we applied a precise 3-D scanner to produce coin height-map images, since detecting a counterfeit coin using 2D image processing is nearly impossible in some cases, especially when the coin is damaged, corroded or worn out. In this research, we propose some 3-D approaches to model and analyze several large datasets. In our first and second methods, we aimed to solve the degradation problem of shiny coin images due to the scanning process. To solve this problem, first, the characters of the coin images were straightened by a proposed straightening algorithm. The height-map image, then, was decomposed row-wise to a set of 1-D signals, which were analyzed separately and restored by two different proposed methods. These approaches produced remarkable results.
We also proposed a 3-D approach to detect and analyze the precipice borders from the coin surface and extract significant features that ignored the degradation problem. To extract the features, we also proposed Binned Borders in Spherical Coordinates (BBSC) to analyze different parts of precipice borders at different polar and azimuthal angles. We also took advantage of stack generalization to classify the coins and add a reject option to increase the reliability of the system. The results illustrate that the proposed method outperforms other counterfeit coin detectors.
Since there are traces of deep learning in most recent research related to image processing, it is worthwhile to benefit from deep learning approaches in our study. In another proposed method of this thesis, we applied deep learning algorithms in two steps to detect counterfeit coins. As Generative Adversarial Network is being used for generating fake images in image processing applications, we proposed a novel method based on this network to augment our fake coin class and compensate for the lack of fake coins for training the classifier. We also decomposed the coin height-map image into three types of Steep, Moderate, and Gentle slopes. Therefore, the grayscale height-map image is turned to the proposed SMG height-map channel. Then, we proposed a hybrid CNN-based deep neural network to train and classify these new SMG images. The results illustrated that a deep neural network trained with the proposed SMG images outperforms the system trained by the grayscale images. In this research, the proposed methods were trained and tested with four types of Danish and two types of Chinese coins with encouraging results.


identifier: Amudala, Srikanth  (2020) Variational techniques for medical and image processing applications using generalized Gaussian distribution.  Masters thesis, Concordia University.

abstract:
In this thesis, we propose a novel approach that can be used in modeling non-Gaussian
data using the generalized Gaussian distribution (GGD). The motivation behind this work is the shape flexibility of the GGD because of which it can be applied to model different types of data having well-known marked deviation from the Gaussian shape.

We present the variational expectation-maximization algorithm to evaluate the posterior distribution and Bayes estimators of GGD mixture models. With well defined prior distributions, the lower bound of the variational objective function is constructed. We also present a variational learning framework for the infinite generalized Gaussian mixture (IGGM) to address the model selection problem; i.e., determination of the number of clusters without recourse to the classical selection criteria such that the number of mixture components increases automatically to best model available data accordingly. We incorporate feature selection to consider the features that are most appropriate in constructing an approximate model in terms of clustering accuracy. We finally integrate the Pitman-Yor process into our proposed model for an infinite extension that leads to better performance in the task of background subtraction. Experimental results show the effectiveness of the proposed algorithms.


identifier: Hosseini, Hiva  (2020) Using Fuzzy AHP for Investigating Barriers to the Development of Smart Mobility in Montrèal.  Masters thesis, Concordia University.

abstract: Mobility is a vital issue for residents and local governments, and it has an impact on sustainability, economy, and lifestyle [1]. Smart mobility focuses on real-time data accessibility [1]. Public Accessibility of real-time data helps smart mobility players to provide efficient, safe, sustainable, and high-quality transportation services [1]. This research aims to investigate and prioritize smart mobility barriers in the city of Montrèal to help decision-makers, policy planners, and smart mobility players to establish effective approaches for safer, smarter, and modern transportation systems in Montrèal [2]. In this research, firstly, 39 smart mobility barriers are identified using an integrative literature review. Secondly, the list of barriers modified by experts from the public, private, and multinational sectors to be compatible with mobility system and infrastructure in Montrèal. Lastly, the Fuzzy Analytical Hierarchy Process (AHP) method is used to prioritize identified barriers. Results show that financial barriers have a major impact on smart mobility development in Montrèal followed by “legal & regulatory”, “technical & technological”, “administrative”, “information & awareness”, “others”, “social”, “policy”, “environmental”.


identifier: Karimipourfard, Golnoosh  (2020) Development of Resuspension Technique for On-site Phosphorus Remediation of Eutrophic Lakes.  Masters thesis, Concordia University.

abstract:
Eutrophication is the enrichment of nutrients, especially phosphorus, in aquatic systems, with harmful effects on surface water bodies such as lakes. Usually, 80-90% of the phosphorus is incorporated into lake bottom sediments, which could be released to the water body through different mechanisms.
Resuspension technique has been introduced as a new approach to solving the eutrophication issue by treating the bottom sediments. The basic idea of resuspension method is that finer sediment particles tend to adsorb more contamination due to their higher effective surface area and ionic attraction. In resuspension systems, finer particles are targeted for removal from the aquatic environment.  In this experimental study, the resuspension mechanism was simulated in a confined water column. This study aims to identify the most effective particle sizes and the optimum condition to capture the sediment particles, and to investigate the feasibility and efficiency of the resuspension method.
The results show that sediment particles of around 16 µm to 30 µm in size carried higher concentrations of phosphorus than sediment particles of other sizes. Resuspension technique could successfully reduce the total concentration of phosphorus contaminations. The optimum settling time to decrease the phosphorus level by 15% was calculated as 14 minutes, considering 30% slurry removal. The phosphorus concentration decreased by 15% by removing only 7% of the fine particles. Phosphorus contamination carried by the suspended sediment particles was about 2.4 times higher than the bulk sediments. The experimental results confirmed the accuracy of theoretical calculations. The suspended particle matter (SPM) removed through the resuspension system was passed through one layer of different filters. Filters with a pore size of 13.3 µm decreased the phosphorus level of SPM from 6.57 mg/L to 0.027 mg/L. Generally, filters with smaller pore sizes were more effective for decreasing the phosphorus contamination.
The resuspension method showed desirable results for the removal of phosphorus from bottom sediments. A small amount of sediment removed from the system, and no chemical substances were employed. Consequently, the resuspension method causes less destruction in the aquatic ecosystem.


identifier: Brodie, Patrick ORCID: https://orcid.org/0000-0001-5359-754X <https://orcid.org/0000-0001-5359-754X>  (2020) Wild Tides: Media Infrastructure and Built Space in Post-Financial Crisis Ireland.  PhD thesis, Concordia University.

abstract:
Wild Tides articulates the ways in which the circulatory logics of contemporary capitalism are mapped within the Republic of Ireland (Ireland) through the spatial lens of media industries and their infrastructures. Building upon existing critical research on media infrastructure, financialization, and logistics, the dissertation unpacks the shifting cultural and economic policy logics in Ireland since the global financial crisis of 2007-2008. The effects of this crisis in the country revealed the extent to which Ireland’s political, economic, cultural, and environmental futures were tied to the turbulence of global financial markets and trade. The dissertation addresses this post-crisis environment through three case study chapters: 1) creative industries and media production environments around Dublin (film studios, post-production hubs, urban formations, peripheral industries and infrastructures); 2) media and cultural policy across urban and rural spaces (media production funding, tax breaks, transnational agreements, labour conditions); 3) media infrastructures and the technology industry (data centers, tech industry clusters, pro-business planning, environmental discussions).

The dissertation builds on research emphasizing the integral role that infrastructure plays within the social and spatial environments of contemporary life under global capitalism, contributing insights relevant to media industry studies, infrastructure studies, science and technology studies, and human geography. Responding to formative media studies questions as to the cultural role of media within contemporary transnational capitalism and the decline of nation-state governance, the dissertation applies political economy and critical geography to expand understandings of the emplaced role of labour, communities, and landscapes within media and its infrastructures. Drawing on extensive site-specific fieldwork in Ireland, policy and discourse analysis, and an interdisciplinary theoretical grounding, my approach, as well as looking inside the media infrastructures to see how they work, also looks at intensities and externalities: the edges of governance, where local culture interacts with sites of infrastructural development.

The dissertation finds that the infrastructures of production and circulation of culture and media in Ireland are deeply intertwined via financial and business policy arrangement in the Irish government both before and after the crisis, revealing entanglements of the state and transnational corporations and implications of their complex cooperation on-the-ground. The interlocking operations of the Irish state and transnational corporations effectively naturalize the role of foreign direct investment (FDI) in determining Ireland’s spatial, political, cultural, and economic futures. The thesis unpacks these financialized logics of corporate and cultural development as they are enacted and lived in an environment of widespread economic austerity and more recent recovery. The concluding chapter of the dissertation unpacks the cultural politics of data centers in Ireland and the role of the tech industry within global climate change. Speaking to the role of media infrastructure in Ireland’s present and future, it proposes ways to think about infrastructure that do not play into the shifting tides of the global economy and contribute to ongoing environmental damage.


identifier: Dixit, Abhinandan ORCID: https://orcid.org/0000-0001-9616-0559 <https://orcid.org/0000-0001-9616-0559>  (2020) Analysis and Design of High Efficiency Grid-to-Vehicle (G2V) Plug-in Chargers for Local e-Transportation.  Masters thesis, Concordia University.

abstract:
Electric transportation worldwide has witnessed a tremendous increase in the use of electric vehicles (EV's) due to increased awareness of environmental issues. Road EV's compromise a broad spectrum of vehicles right from two-wheelers three-wheelers (rickshaws/Auto/Trio), cars and electric buses. E-Rickshaw has gained popularity in the Asian market post-2010 because of their symbolic resemblance with traditional auto-rickshaw. The fast growth of the market is principally pushed by the low ownership cost of electric three-wheelers, falling battery prices, and favorable government policies and support. These EVs run on low-cost 48 V, 120 Ah lead acid battery packs having low depth-of-discharge (DOD). Hence, frequent battery charging becomes essential for such EVs. Conventional battery chargers available in the market utilize flyback converter based topologies in order to charge such battery packs. On one hand such battery chargers are easy to implement, these topologies fail to achieve unity power factor (UPF) operation leading to high total harmonic distortion (THD) and poor input power quality at the input. Thus active power factor correction (PFC) becomes a vital constituent in AC-DC converters. By understanding the constraints posed by continuous current mode (CCM) based battery chargers, the proposed converters are designed to operate in discontinuous current mode (DCM) because of its evident benefits such as inherent PFC, zero current turn-on and zero diode reverse recovery losses. By omitting sensors at the input and utilizing only the output sensors, regulated voltage or current can be obtained which makes the system cost-effective and improves its reliability and robustness to high frequency noise.
This thesis presents both isolated and non-isolated battery charger for local e-transportation EVs utilizing 48 V lead acid battery pack. At first, a non-isolated single-stage interleaved buck-boost float charger is proposed by considering the advantages such as reduced current stresses, minimum number of semiconductor devices and absence of bulky high frequency transformer. DCM operation of the proposed converter ensure UPF operation for variable input voltage and utilizing just a single sensor makes this charger configuration economical and easy to implement. However, such a configuration had high current stress on the semiconductor devices leading to increased thermal requirement and reduced efficiency at light loads. Thus addressing these problems, a high efficiency two-stage battery charger is proposed. The battery charger uses an interleaved DCM buck-boost converter in order to achieve PFC at variable input voltage, whereas the second stage is an unregulated half-bridge LLC resonant converter which provides isolation as well as soft-switching for the primary switches. Synchronous rectification (SR)  along with only capacitive filter is used on  center tapped transformer secondary to improve converter efficiency. Due to DCM of the front-end AC-DC converter achieves zero current turn-on of the switches and DC-DC converter switches achieve zero voltage turn-on because of the LLC resonant. The proposed battery charger implements constant current (CC) and constant voltage (CV) method of charging using simple PI controllers, thus making it suitable for commercial use. Small signal models for both the battery charger configurations are developed using the current injected equivalent circuit approach and a detailed controller design is illustrated. Simulation results using PSIM11.1 software and experimental results from proof-of-concept laboratory hardware prototypes are provided in order to validate the reported analysis and design which demonstrates their performance.


identifier: Ferrar, Saskia J. ORCID: https://orcid.org/0000-0002-3687-0089 <https://orcid.org/0000-0002-3687-0089>  (2020) Conflict Dynamics in Mother-Child and Sibling Dyads: The Interplay Between Observed Behavior and Emotional Expression and Links with Children’s Socioemotional Development.  PhD thesis, Concordia University.

abstract:
In a series of two observational studies, the present dissertation examined the interplay between emotional expressions and behavior during conflict between children and their family members. Conflict discussions were observed at two developmental periods (preadolescence and adolescence) and in two types of relationships (mother-child and sibling), to assess how family members respond to their own and their partners’ emotional expressions. Within-family similarities in responses to negative emotions as well as links with youths’ socioemotional development were also examined.
The participants in the two present studies were drawn from an ongoing forty-year longitudinal study of families in Montréal, Québec. In Study 1, preadolescents (aged 9 to 13 years) were observed during conflict discussions with their mothers, and questionnaire measures of their temperament and socioemotional functioning were collected at one time point prior (aged 6 to 10 years) and one time point subsequent (aged 11 to 16 years) to the observational measurements. In Study 2, early adolescents (aged 12 to 15 years) were observed during conflict discussions with their siblings and with their mothers. In both studies, participants’ verbal conflict behaviors and emotional expressions were coded continuously. Time-window sequential analysis was used to identify how participants responded to their own and their interaction partners’ emotional expressions. In Study 1, across-dyad differences in responses to negative emotions were associated with youths’ socioemotional functioning over time, and in Study 2, within-family similarities in responses to negative emotions were examined.
Results from both studies indicated that overall, family members escalated conflict more (i.e., disagreed and confronted) and made more assertive (i.e., analytic) remarks when they appeared angry (i.e., displayed frowning/upset affect), and were more conciliatory and avoidant when they appeared sad. Neutral affect predicted the most conflict de-escalating behavior (i.e., analytic and conciliatory remarks), while positive affect promoted both de-escalating behavior and avoidance. Links between individuals’ behavior and their interaction partners’ emotional expressions were generally similar, yet weaker than responses to their own emotions. Differences between mother- child versus sibling conflict patterns, as well as between mother-child conflict before and after the transition to adolescence, suggested that family conflict dynamics are influenced by relationship type as well as developmental timing. Results from Study 1 also indicated that mothers’ tendency to escalate conflict when angry was associated with difficult child characteristics in earlier childhood and socioemotional difficulties in adolescence. Further, maternal and child de-escalation following sadness predicted socioemotional adjustment in adolescence. Furthermore, Study 2 identified many within-family similarities in responses to negative emotions, yet relatively few similarities in how youth responded across the two conflict contexts (i.e., with their mothers and with their siblings).
Findings are discussed in relation to goal-based theories of emotion, as well as dynamic systems, transactional, and family systems perspectives on child development. The present dissertation makes a substantive contribution to our understanding of family conflict dynamics across the transition to adolescence, by illustrating how constructive and destructive conflict is linked to several contextual variables, including emotion, child characteristics, and relationship type. In addition, they show how individual differences in the management of negative emotions are tied to youths’ socioemotional development and to family functioning. Taken together, the present findings have a number of clinical implications that can help inform interventions aimed at promoting adaptive family conflict communication and psychological well-being.


identifier: Girard Vermeil, Alexandre  (2020) Moon Prism Boys: The Magical Boy as Queering Device.  Masters thesis, Concordia University.

abstract: Through a queer lens, this thesis examines the role that the magical boy character plays in the manga Magical Boy Majorian (2007-2008), and in season 15 of the popular magical girl genre anime franchise Pretty Cure, entitled Hugtto! PreCure (2018-2019). Using queer theory and gender studies research, this thesis argues that the magical boy figure challenges problematic forms of masculinity by performing what I call ‘shōjoness,’ a particular form of feminine aesthetic mainly found in shōjo (for girls) manga, and anime. The thesis contends that henshin (transformation) sequences allow a space for gender negotiation, which disrupts a heteronormative structure through the crossing, and blending, of gender expression and form. Acting as what Sedgwick calls a queer survival object (1993), the magical boy becomes a site for queer representation, but also what I call a queering device. In other words, this thesis argues that Majorian and Hugtto serve as escapes from the burden of heteronormativity, while also holding to some extent the power to inspire change in attitudes toward queerness within, and beyond the pages of the manga, or the screen of anime. Analyzing key scenes from both works mentioned above, this thesis explores how medium specificity (manga structure, and animation techniques) renders queerness by contrasting it with more standard ways of drawing, and animating, consequently challenging normative ways of being. Informed by Scott McCloud’s theorizing of the gutter, Thomas Lamarre’s work on the interval, and Sara Ahmed’s discussion on queer orientation, Moon Prism Boys frames particular forms of interstices as spaces that have the potential to positively disturb a monolithic form of masculinity when brought into contact with queerness.


identifier: Afifi, Islam Sayed Hassan Mohammed ORCID: https://orcid.org/0000-0001-6519-0915 <https://orcid.org/0000-0001-6519-0915>  (2020) 30 GHz Printed Ridge Gap Components and Antennas for Imaging Systems.  PhD thesis, Concordia University.

abstract:
Working at millimeter waves (MMW) has gained massive attention for wireless communications and imaging systems. For imaging systems, MMW can be used for security to provide good resolution images and detect concealed weapons as it can penetrate common clothes and reflect from the human body and metal objects. Moreover, MMW is safe for human health, contrary to conventional X-ray imaging, which uses an ionized wave. Thus, it has a harmful effect on human health.

This research is focusing on building an active wide-view angle millimeter-wave imaging system with a small area of mechanical movement to reduce the data collection time. The imaging system is composed of three main parts: 1) the millimeter-wave components and antennas, 2) the mechanical part for moving the antennas and performing the scan of the imaging area, and 3) the imaging reconstruction algorithm.

In order to have an efficient imaging system, the printed ridge gap technology (PRGW) is used to build the imaging system components and antennas. High efficiency coaxial to PRGW transition with a fractional bandwidth of 59.22% at 32.25 GHz is designed to feed the system components. For the transmitting part of the imaging system, a moderate gain PRGW differential feeding planar aperture antenna and a wideband rat-race coupler are designed. The antenna, the rat-race, and the coaxial transition are combined to form the transmitting part, then fabricated and measured. The resulted bandwidth is from 25.62 to 34.34 GHz with a return loss better than 10 dB, a maximum gain of 12.28 dBi, and 3-dB gain bandwidth from 25.62 to 33.77 GHz.

For the receiving antenna, a PRGW Butler matrix and its components (directional couplers, 45◦ phase shifters, and crossovers) are designed. A semi-log periodic antenna fed by the PRGW is designed as the radiating element. The PRGW components, the coaxial transition, and the antennas are combined to form the receiving part of the imaging system, which is fabricated and measured. The resulting beam directions are at ±13◦ and ±36◦, at the center frequency (30 GHz). The return loss and the isolations are better than 10 dB over the frequency range from 26.1 to 33.5 GHz.

For the imaging reconstruction algorithm, a synthetic aperture radar algorithm is used. Two tests are carried out, one uses CST simulation results, and the other uses measured data from the Concordia antenna chamber lab. The results show an output resolution of 0.6 λ.

Finally, the whole imaging system is built with the designed differential feeding antenna as the transmitter, the designed Butler matrix as the receiver, and the synthetic aperture algorithm as the image reconstruction algorithm. The performance network analyzer (PNA) is used to collect the data (s-parameters) required to reconstruct the image, and the antenna range controller system (NSI 5913) is used to mechanically scan the imaging area. The imaging system is used to scan a mannequin carrying an object shaped like a pistol and a knife. The results show that the two objects are detected.


identifier: Rahman Wasee, Farhan  (2020) Estimating Reflectance Properties and Reilluminating Scenes Using Physically Based Rendering and Deep Neural Networks.  Masters thesis, Concordia University.

abstract:
Estimating material properties and modeling the appearance of an object under varying illumination conditions is a complex process. In this thesis, we address the problem by proposing a novel framework to re-illuminate scenes by recovering the reflectance properties. Uniquely, following a divide-and-conquer approach, we recast the problem into its two constituent sub-problems.

In the first sub-problem, we have developed a synthetic dataset of spheres with realistic materials. The dataset has a wide range of material properties, rendered from varying viewpoints and under fixed directional light. Images from the dataset are further processed and used as reflectance maps used during the training process of the network.

In the second sub-problem, reflectance maps are created for scenes by reorganizing the outgoing radiances recorded in the multi-view images. The network trained on the synthetic dataset, is used to infer the material properties of the reflectance maps, acquired for the test scenes. These predictions are reused to relight the scenes from novel viewpoints and different lighting conditions using path tracing.

A number of experiments are conducted and performances are reported using different metrics to justify our design decisions and the choice of our network. We also show that, using multi-view images, the camera properties and the geometry of a scene, our technique can successfully predict the reflectance properties using our trained network within seconds. In the end, we also present the visual results of re-illumination on several scenes under different lighting conditions.


identifier: Wang, Yunan  (2020) The Effect of Political Connection on Derivatives Hedging in the Chinese Market.  Masters thesis, Concordia University.

abstract: This paper examines the effects of political connection on derivatives hedging in the Chinese market from 2007 to 2018. Using a sample of privately-owned enterprises (POEs), this study hypothesizes and argues that the top managers or non-independent board directors who have current or previous political work experience exert a negative influence on a firm’s hedging incentives since these firms may benefit from governmental bailouts by building connections and resultantly have fewer motives to hedge. Similarly, state-owned enterprises (SOEs) are considered to have natural political connections as their ownership rests in the hands of the government, and therefore should have even fewer incentives to hedge compared with POEs. However, the results we obtain in this thesis do not support this widely held assumption. Interestingly, the financial background of top managers and board directors appears to have different effects on hedging activities in POEs and SOEs.


identifier: Ghodsi, SeyedehNegar  (2020) Application of Condition-based Maintenance in Control of a Supply Chain Network under Stochastic Disruption.  Masters thesis, Concordia University.

abstract:
The thesis develops novel and proactive optimal control policies for a partially observable facility, which is subject to stochastic disruptions. Unlike traditional Supply Chain
Networks (SCN), where established facilities are considered to be continuously available, a more practical scenario is developed. More specifically, in the proposed frameworks, the
aforementioned assumption is relaxed such that the facilities are subject to stochastic disruptions potentially leading to costly failures. In such practical scenarios, it is critical and of paramount importance for the established facilities to operate with the highest achievable
reliability in the presence of disruptions and degradation. In this regard, this thesis provides a conceptual framework to obtain an optimal control policy for an already established facility subject to stochastic disruptions/degradation such that the disruptions have a direct effect on the connection links within the SCN. The level of degradation of a facility is modeled as a N state continuous time hidden-Markov process with N −1 operational and unobservable states together with one observable failure state. The facility is monitored periodically to observe the level of degradation. If the degradation level exceeds a critical state, a preventive action, namely partial fortification, will be performed. On the other hand, when the degradation level exceeds the failure state, a corrective action, namely full fortification, will be performed which brings the facility to the healthy state. The model is extended to the scenario where an integrated model of Statistical Process Control (SPC) and maintenance planning is considered and the optimal control limit policy is achieved based on a novel Bayesian control chart. The control problems under consideration are formulated in a Partially Observable Markov Decision Process (POMDP) framework to find the optimal preventive level in order to minimize the long-run expected average cost. A comprehensive sensitivity analysis is performed to evaluate the performance of proposed
models.


identifier: Alhebaishi, Nawaf  (2020) Threat Modeling for Cloud and NFV Infrastructures.  PhD thesis, Concordia University.

abstract:
Today’s businesses are increasingly relying on the cloud as an alternative IT solution due to its flexibility and lower cost. Compared to traditional enterprise networks, a cloud infrastructure is typically much larger and more complex. Understanding the potential security threats in such infrastructures is naturally more challenging than in traditional networks. This is evidenced by the fact that there are limited efforts on threat modeling for cloud infrastructures. My doctoral research will tackle several issues related to this.

In the first topic, we have conducted comprehensive threat modeling exercises based on two representative cloud infrastructures using several popular threat modeling methods, including attack surface, attack trees, attack graphs, and security metrics based on attack
trees and attack graphs, respectively. In addition, we show how hardening solution can be applied based on the threat models and security metrics through extended exercises. Such results may not only benefit the cloud provider but also embed more confidence in cloud tenants by providing them a clearer picture of the potential threats and mitigation solutions.

In the second topic, we take the first step towards understanding and mitigating the insider threats of remote administrators in clouds. First, we model the maintenance task assignments and their corresponding security impact due to privilege escalation. Second, we mitigate such impact through optimizing the task assignments with respect to given
constraints. Finally, the simulation results demonstrate the effectiveness of our solution in various scenarios.

In the third topic, we focus on modeling and mitigating security threats unique to network functions virtualization (NFV), which provides virtualization of network functions based on cloud infrastructures. First, we model both cross-layer and co-residency attacks on the NFV stack. Second, we mitigate such threats through optimizing the virtual machine (VM) placement with respect to given constraints. The simulation results demonstrate the effectiveness of our solution.


identifier: Antoun, Joseph ORCID: https://orcid.org/0000-0002-5638-5734 <https://orcid.org/0000-0002-5638-5734>  (2020) Electric Vehicles Mass Integration: Impact on the Power Grid and Charging Infrastructure Availability.  Masters thesis, Concordia University.

abstract:
Electric Vehicles (EV) are gaining large popularity in the transportation sector, and that raises numerous concerns for the power sector. The repercussions of such increased demand are notable at the distribution side with different aspects of EV usage. To accommodate this increased number, multiple Charging Stations (CS) are being deployed to assist users in enhancing their charging experience. However, public stations are underutilized due to lack of useful performance information, such as waiting time, and outlet availability. Therefore, users will favor home charging over public charging. This behavior will come with a drastic increase in power demand on the residential network. In addition, various behaviors of EV users, such as mass charging and preconditioning, can deteriorate the network's Quality-of-Service (QoS). Therefore, the impact of an elevated number of EVs and increased number of level 2 chargers on the residential distribution network is analyzed. Subsequently, the competency of dynamic pricing in handling this such elevated load in investigated. Afterwards, the repercussions from users preconditioning their vehicles during winter is inspected. In addition, we assess the competency of network reconfiguration in holding the network performance within operational range during preconditioning window. The performance of network reconfiguration will degrade when presented with high number of EVs; therefore this elevated number is leveraged through Vehicle-to-Grid (V2G) technology to assist network reconfiguration in balancing the preconditioning load.
Finally, a data-driven performance model for public charging stations is derived to gain more knowledge on its operation. Metrics such as waiting time, reneging probability and blocking probability are derived and analyzed to assist users in their charging processes and operators enhancing the station deployment.


identifier: AGUESSY, MICHAEL NELSON  (2020) ESSAYS ON PRICE DISCOVERY AND MODEL SELECTION IN PRESENCE OF WEAK INSTRUMENTS.  PhD thesis, Concordia University.

abstract:
This thesis organized in three chapters, essentially covers two main fields: finance and econometric theory with an application to macroeconomics. The first chapter proposes a methodology to uniquely measure price discovery, the mechanism by which the price of a security or an asset cross-listed on multiple markets is determined. The second chapter develops an information criterion that remains robust in presence of weaker instruments. Finally, the third chapter illustrates the benefits of optimal instruments' selection in assessing the impact of an example of monetary policy.
Being a process that allows market participants to uncover the real worth of an asset in a timely manner, the price discovery may lead to arbitrage opportunities. As such, the Information Share (IS) commonly used to measure it, needs to be as accurate as possible to help mitigate related market inefficiencies.

In the first chapter of this thesis, we investigate the identification issues encountered by the IS due to its sensitivity to price ordering. This translates to price innovations vectors leading to a serious lack of robustness of the IS metric. Exploiting some statistical features of price innovations, we propose to use Independent Component Analysis (ICA) in order to decompose the residuals into independent signals. Compared to leading measures in the literature, our approach is shown to perform well in the standard two-market data framework. We also obtain consistent results while extending our simulations to larger number of markets framework, notably the three-market set-up. We finally confirm our findings by studying the mechanism of price discovery in two analogous empirical applications. The first analyzes futures and spot prices in the European Union Allowances (EUAs) market for CO2 emissions, and the second concentrates on three Exchange Traded Funds (ETFs) tracking the performance of the Russell 2000 index. Our evidence suggests that futures prices and the IWM (ETF issued by iShares), respectively dominate their companions in contribution to price discovery.

The second chapter is motivated by the fact that the usual exogeneity assumption is essential to the least squares estimator as it guarantees its consistency. However, when this condition fails, the explanatory variable is said to be endogenous and Instrumental Variable (IV) regressions is one of the methods available to the researcher to obtain consistent estimates. In response to the importance of the instruments selection step in the construction of a good IV estimator, we propose the alternative Relevant Moment Selection Criterion (aRMSC). This information criterion improves model selection when instruments are only weakly correlated with the endogenous variable. Through Monte Carlo simulations, we first illustrate that existing information criteria are not robust to these types of issues; naively selecting the larger models. We benefit from recent development on the importance of the strength of identification in achieving efficient estimation, and leverage it to evaluate how this may affect instruments selection when the candidate instruments available to the researcher are equally weakened or a pool of instruments with various strengths. Our evidence suggests that despite their weakness some instruments still contribute to improving the estimator's efficiency, in such a way that the selection of the most parsimonious model is possible.

In the final chapter of this thesis, we first illustrate the performance of our proposed information criterion in a macroeconomic application. Moreover, we study empirically the relationship between news from forward guidance and monetary policy. We account for interactions both between various macroeconomic variables while considering their own lagged values using a structural vector autoregressive (VAR) model including interest rates, consumer price index, industrial production and excess bond premium. Our analysis relies on Gertler and Karadi’s (2015) high frequency identification (HFI) approach for monetary policy shocks to extend monetary policy indicators to the 2 year government bond rate even though authors initially considered it as facing weak instruments issues. The aRMSC allows us to identify relevant instruments in the VAR model with the 2 year government bond rate and compare our results to those predicted with the 1 year rate, a stronger instrument. We also consider the limited information maximum likelihood estimator (LIML) to improve the instruments' selection. All together, our results highlight that the model based on the optimal set of instruments in comparison to the model with the naive inclusion of all instruments from the candidate set, produces more accurate impulse responses for economic and financial variables regardless of the estimator used to obtain the alternative information criterion.


identifier: Singh, Hardeep  (2020) Salen-type Metal Complexes with a Hydroxylamine Function and their Application in Aerobic Alcohol Oxidation.  Masters thesis, Concordia University.

abstract:
Aromatic compounds containing a redox-active hydroxylamine functionality are of great importance in biology and in synthetic organic chemistry. For example, arylhydroxylamines can act as a nitric oxide donor in mammals under certain conditions. They also participate as substrates in organic reactions for C–N bond formation, e.g. the metal-catalyzed nitroso-ene reaction. The redox conversions of arylhydroxylamines are, however, not well studied because of the high reactivity of these compounds and the ensuing formation of undesired side products. To address this challenge and gain insight into the redox behaviour of arylhydroxylamines in a systematic manner, we designed a family of ligands that possess an arylhydroxylamine function and can accommodate a variety of metal ions. This thesis reports the synthesis of these ligands, their complexation with three metal ions and the capacity of the complexes to catalyze a test organic reaction, the aerobic oxidation of alcohols into aldehydes.
Ligand design is based on the well-known family of salen ligands. We installed a pendant nitro group (ArNO2) on one side of the ligand via a sulphonamide linkage, while substitutions on the salicylimine side afford structural and electronic variety. Partial reduction of the nitro group provides access to the corresponding arylhydroxylamine (ArNHOH) ligand, but the reduction conditions have to be fine-tuned to avoid over-reduction to the amine (ArNH2) function. This was achieved either by catalytic hydrogenation over a poisoned palladium/charcoal catalyst or by stoichiometric transfer hydrogenation with zinc and ammonium formate. After optimization, these methods yielded four NHOH-containing ligands.
Subsequent complexation of the ligands with copper(II), nickel(II) and zinc(II) did not trigger a reaction with the redox-active NHOH function, contrary to the common belief that hydroxylamines disproportionate in the presence of metal ions. These unique complexes have been characterized by single-crystal X-ray diffraction, 1H-NMR, and mass spectrometry. These studies reveal that the hydroxylamine function is engaged in an intramolecular hydrogen bond, which we hypothesized is responsible for the metastability of the NHOH function.
The complexes were screened as pre-catalysts for the aerobic oxidation of primary and benzylic alcohols to produce aldehydes. Only with benzylic alcohols was significant turnover observed, and only with the copper complexes (copper is known to enable oxygen-activation turnover). More importantly, control experiments reveal that the NHOH function is crucial for catalysis, which portends a similar mechanism as in the Markó alcohol oxidation system.
Further studies will focus on characterizing the fate of the NO bond during turnover, with the prospect of designing a novel family of catalysts for two-electron oxidation reactions.


identifier: He, Tong  (2020) UAS Flight Path Planning and Collision Avoidance Based on Markov Decision Process.  Masters thesis, Concordia University.

abstract:
The growing interest and trend for deploying unmanned aircraft systems (UAS) in civil applications require robust traffic management approaches that can safely integrate the unmanned platforms into the airspace. Although there have been significant advances in autonomous navigation, especially in the ground vehicles domain, there are still challenges to address for navigation in a dynamic 3D environment that airspace presents. An integrated approach that facilitates semi-autonomous operations in dynamic environments and also allows for operators to stay in the loop for intervention may provide a workable and practical solution for safe UAS integration in the airspace.
This thesis research proposes a new path planning method for UAS flying in a dynamic 3D environment shared by multiple aerial vehicles posing potential conflict risks. This capability is referred to as de-confliction in drone traffic management. It primarily targets applications such as UAM [1] where multiple flying manned and/or unmanned aircraft may be present. A new multi-staged algorithm is designed that combines AFP method and Harmonic functions with AKF and MDP for dynamic path planning. It starts with the prediction of aircraft traffic density in the area and then generates the UAS flight path in a way to minimize the risk of encounters and potential conflicts. Hardware-in-the-loop simulations of the algorithm in various scenarios are presented, with an RGB-D camera and Pixhawk Autopilot to track the target. Numerical simulations show satisfactory results in various scenarios for path planning that considerably reduces the risk of conflict with other static and dynamic obstacles. A comparison with the potential field is provided that illustrates the robust and fast of the MDP algorithm.


identifier: Phan, Thi Hai Yen  (2020) Portfolio Allocation with Temporary Price Impact.  Masters thesis, Concordia University.

abstract: This thesis analyzes an investor’s portfolio choice and liquidity premium in the presence of an illiquid stock. Illiquidity is modelled by means of convex transaction costs which an investor has to pay for trading a stock. An investor is assumed to trade in the presence of a stochastic endowment which is used to generate long-term trading demands. I find that the endowment generates long-term trading incentives only if there exist correlations between the endowment and stocks returns. These incentives result in the liquidity premium which makes only a fraction of a percent of the risk premium. The portfolio choice and the conditional liquidity premium can be determined in a closed form in the case of the absence of these correlations and the main economic intuition behind the findings is confirmed.


identifier: Duchaussoy, Quentin  (2020) Security and Privacy Analysis of Parental Control Solutions.  Masters thesis, Concordia University.

abstract: For parents of young children and adolescents, the digital age has introduced many new challenges, including excessive screen time, inappropriate online content, cyber predators, and cyberbullying. To address these challenges, many parents rely on numerous parental control solutions on different platforms, including parental control network devices and software applications on mobile devices and laptops. While these parental control solutions may help digital parenting, they may also introduce serious security and privacy risks to children and parents, due to their elevated privileges and having access to a significant amount of privacy-sensitive data. In recent years, attacks and security flaws concerning this type of applications have flourished, however no systematic study for the security and privacy of these parental control solutions has been carried out to date. In this thesis, we present an experimental framework for systematically evaluating security and privacy issues in parental control software and hardware solutions. Using the developed framework, we provide the first comprehensive study of parental control solutions on multiple platforms including network devices, mobile apps, Windows applications and web extensions. We analyze a representative dataset of each type of solution and build a security and privacy state-of-the-art of each environment. Our analysis uncovers pervasive security and privacy issues that can lead to leakage of private information, and/or allow an adversary to fully control the parental control solution, and thereby may directly aid cyberbullying and cyber predators.