identifier: Shu, Chang ORCID: https://orcid.org/0000-0003-3807-2002 <https://orcid.org/0000-0003-3807-2002>  (2021) Assessment of the Effects of Extreme Heat Events on Buildings.  PhD thesis, Concordia University.

abstract: Due to urbanization and global warming, extreme heat events, e.g., heat waves, in the urban area tend to occur much more intensively and frequently, imposing a great threat to the health and safety of urban dwellers. The warming temperature also leads to a deteriorating of the indoor thermal conditions. Studies on indoor overheating should be conducted to figure out the interactions between the outdoor environment and indoor conditions and estimate possible approaches to optimize the design and operation of the building system adapting to the changing climate. Considering the building and construction assets are typically designed for a long period over several decades or even centuries. The resiliency of the buildings to extreme conditions should be reconsidered under the future climate conditions, therefore the future weather inputs should be critical for the building overheating study, and any possible mitigation intervenes should be re-evaluated under different future scenarios to evaluate the robustness of the decision and detect the potential risks. The study includes a procedure to select buildings from Montreal city for the field monitoring study, followed by the outcome of building information surveys and site visits. After an overall evaluation of these buildings, 6 school buildings, 6 hospital buildings and 3 residential social housings have been selected for further studies. The measured data are used for the overheating assessment of these buildings and the calibration of these real building models. The measured data exhibited strong evidence of overheating in existing building stocks in Montreal, showing the necessity for further investigation to mitigate the overheating. To consider the spatial distribution in urban climate, the study developed a high-resolution regional climate model for the Montreal and Ottawa region and elaborated the importance of preserving the urban effect in weather files for building studies. The generated climate dataset can be used as the input of EnergyPlus building simulations to evaluate the spatial-temporal pattern of indoor overheating. The validated climate model can also be extended for the future projection of urban scale overheating studies. In the future, overheating mitigation strategies can be applied to the baseline model to evaluate their effectiveness on both historical and future climate in the long run with the proposed workflow and the climate dataset generated by this study.


identifier: Shao, Jun ORCID: https://orcid.org/0000-0002-8620-1123 <https://orcid.org/0000-0002-8620-1123>  (2021) Wavelet-based Multi-level GANs for Facial Attributes Editing.  Masters thesis, Concordia University.

type: Thesis NonPeerReviewed

abstract:
Recently, both face aging and expression translation have received increasing attention from the computer vision community due to their wide applications in the real world.

For face aging, age accuracy and identity preserving are two important indicators. Previous works usually rely on an extra pre-trained module for identity preserving and multi-level discriminators for fine-grained features extraction. In this work, we propose a cycle-consistent loss based method for face aging with wavelet-based multi-level facial attributes extraction from both generator and discriminators.
The proposed model consists of one generator with three-level encoders and three levels of discriminators with an age and a gender classifier on top of each discriminator.
Experiment results on both MORPH and CACD show that the application of multi-level generator can improve the identity preserving effects in face aging and reduce the training time significantly by eliminating the rely of an identity preserving module.
Our model can outperform most of the existing approaches including the state-of-the-art techniques on two benchmark aging databases in terms of both aging accuracy and identity verification confidence, demonstrating the effectiveness and superiority of our method.

In real world, expression synthesis is hard due to the non-linear properties of facial skin and muscle caused by different expressions. A recent study showed that the practice of using the same generator for both forward prediction and backward reconstruction as in current conditional GANs would force the generator to leave a potential "noise" in the generated images, therefore hindering the use of the images for further tasks. To eliminate the interference and break the unwanted link between the first and second translation, we design a parallel training mechanism with two generators that perform the same first translation but work as a reconstruction model for each other. Additionally, inspired by the successful application of wavelet-based multi-level Generative Adversarial Networks(GANs) in face aging and progressive training in geometric conversion, we further design a novel wavelet-based multi-level Generative Adversarial Network (WP2-GAN) for expression translation with a large gap based on a progressive and parallel training strategy. Extensive experiments show the effectiveness of our approach for expression translation compared with the state-of-the-art models by synthesizing photo-realistic images with high fidelity and vivid expression effect.


identifier: Wu, Jundong ORCID: https://orcid.org/0000-0002-5268-2731 <https://orcid.org/0000-0002-5268-2731>  (2021) Modeling and Control of Liquid Crystal Elastomer Based Soft Robots.  PhD thesis, Concordia University.

abstract:
Soft robots are robotic systems which are inherently compliant, and can exhibit body deformation in normal operations. This type of systems has unprecedented advantage over rigid-body robots since they can mimic biological systems to perform a series of complicated tasks, work in confined spaces, and interact with the environment much more safely. Usually, the soft robots are composed of subsystems including the actuator, the sensor, the driving electronics, the computation system, and the power source. In these subsystems, the actuator is of great importance. This is because in most situations the actuator works to carry out the operations of the soft robot. It decides the functionalities and physical features of the whole system. Meanwhile, other subsystems work to aid the successful functioning of the actuator. Thus, the study on soft robot actuators, especially the modeling and control of soft robot actuators is the key to soft robot applications.

However, characteristics of soft robot actuators vary greatly due to the usage of different actuator materials. These materials include the variable length tendons, rubbers, smart materials, etc.. Among these different materials, smart material based actuators have the advantage of fast response, light weight, and can respond to various types of external stimuli such as electrical signal, magnetic signal, light, heat, etc.. As a result, smart material based actuators have been studied widely for possible soft robot applications.

Recent years, among smart materials, the liquid crystal elastomer (LCE) starts to catch researchers' attention. LCE is a type of smart material which can deform under the stimulation of light. Unlike conventional actuators, the LCE actuator can be separated from the power source, suggesting a simpler and lighter design, possible for applications that are totally different from conventional electro-driven or magneto-driven actuators. However, just like other smart materials, the deformation characteristics of the LCE actuator exhibits a complicated hysteretic behavior highly dependent on environmental factors, which brings difficulty to the modeling and control. Furthermore, the deformation of the photo-responsive LCE actuator is a multi-step process, resulting greater inaccuracy when compared with conventional smart material based actuators. These are huge challenges that need to be overcome for the modeling and control of the LCE actuator, which is still in its preliminary stage.

This dissertation aims to develop suitable modeling and control strategies for the photo-responsive LCE actuator with the purpose of using it in soft robot applications. Here, by looking into the physical nature of the light-induced deformation of the LCE actuator, it can be concluded that LCE's deformation is inherently the macroscopic shape change resulted from the microscopic phase change of LCE molecules. Based on this deformation mechanism, an experimental platform including a computer, an I/O module, a programmable laser, the LCE actuator, a thermal camera, and a laser distance sensor is established to study the modeling and control of the photo-responsive LCE actuator. Experiments are performed and the results show that the deformation characteristics of the LCE actuator indeed exhibit obvious hysteresis, which is dependent on environmental factors.

Based on the deformation mechanism of LCE, basic modeling scheme and positioning control scheme for the photo-responsive LCE actuator are established. For the modeling of the LCE actuator, the goal is to obtain its temperature-deformation relationship and describe the hysteresis with small errors. Here, the average order parameter is introduced to give a quantitative description of the macroscopic average phase of LCE molecules. Then, the key to obtain the temperature-deformation relationship is to first find the relationship between the temperature of the LCE actuator and the average order parameter, and then find the relationship between the average order parameter and the macroscopic deformation. The overall model is the combination of the above two relationships.

According to this modeling scheme, a basic physical model for the photo-responsive LCE actuator is established. This model aims to develop a quantitative model that reflects the actual physics of the LCE actuator. By assuming that the phase transition of LCE molecules is under dynamic equilibrium at each specific moment, a simple analytical relationship between the temperature and deformation of the LCE actuator can be obtained. For this model, the Landau-de Gennes expansion of free energy for nematic LCEs is utilized to calculate the average order parameter. First, under the above assumption, the relationship between the temperature and the average order parameter is obtained. Meanwhile, thermal dynamic analysis gives the relationship between the average order parameter and the deformation. The above two relationships are then combined together to give the overall model. Model parameters are calculated based on nonlinear least squares method. Experimental results show that this model works to give a good prediction of the deformation characteristics.

Based on the above basic model, an improved model is then established to give a more detailed description on the hysteresis by considering the actual dynamic process of the phase transition of LCE molecules. In order to reflect the actual dynamic process, a small variation of the temperature is considered, and the corresponding number of LCE molecules that undergo phase transition is calculated based on thermal dynamic analysis and a polynomial expansion of the transition rate. As a result, a dynamic equation that gives the temperature-deformation relationship is obtained. To obtain the values of model parameters with efficiency, a two-step parameter identification method based on the differential evolution algorithm and nonlinear least squares method is established. Experiments show that the improved model can describe the hysteretic deformation characteristic of the photo-responsive LCE actuator with high accuracy.

Meanwhile, based on the physical nature of the LCE actuator, the positioning control of the photo-responsive LCE actuator is studied. Analysis on the deformation of the LCE actuator from the energy perspective shows that the positioning control of the photo-responsive LCE actuator is a multi-step process, which brings difficulties in control accuracy. To reduce the positioning control errors, a double closed-loop control structure with a feed-forward module is designed for the positioning control of the photo-responsive LCE actuator. Utilizing positioning control scheme together with the developed models, controllers are designed for the positioning control of the photo-responsive LCE actuator. For the proposed double-closed loop structure, the inner loop uses a PID controller to control the temperature of the LCE actuator, the parameters of the inner loop controller are tuned using a stimulation-experiment combined method based on the Hammerstein-Wiener model. Meanwhile, the outer loop consists of a PID controller and a feed-forward controller, the feed-forward controller is a numerical inverse model of the simple physical model that is established in the modeling part, and calculates the target temperature for the inner loop based on the positioning control objective. Parameters of the outer loop controller are directly tuned through experiments. Based on the proposed control strategy, experiments with different control targets are carried out to prove that the proposed controller can achieve the positioning control target with high accuracy. Comparison experiments also show that the proposed double closed-loop structure is faster in response, and has smaller control errors than conventional single closed-loop control structure.

In the end, design guidelines for LCE based soft robots are discussed from the application perspective. Designs of a two-legged walking robot and a light-controlled rolling robot based on the photo-responsive LCE actuator are introduced, conclusions are made together with possible working directions for future studies.


identifier: Gamarooni Pour, Seyed Ali  (2021) Requirements analysis of aviation standards Applicable to Production Organizations.  Masters thesis, Concordia University.

abstract: Standards are the bottleneck of any aviation industrial action. Thus, meeting standards is not an option but an obligation throughout a given product life cycle. This thesis contributed to producing a SIPOC diagram of aviation standards and collecting pertaining information, beginning with international standards, and ending in national codes. Particularly, the requirements which are applicable to a production organization in the aviation industry. A detailed view of the rulemaking process in the aviation industry is provided. The implementation of the standards and the evaluation process are complicated and need considerable resources. Simultaneously, Extra costs and concerns may be imposed on the aviation industry due to improper requirements. Thus, the quality of standards should be comprehensively assessed to obtain a satisfactory final result. System engineering introduces requirement analysis to solve this problem. As a significant contribution, this thesis provides a model of requirement analysis based on the structured strategy. The criteria were defined practically. Diagram, process, sub-process, and techniques including RTM and WBS were either developed or deployed to investigate the conformance of requirements with the criteria. Applying this model, any sort of requirement is evaluated accurately. Plus, it shows a specific and root cause of the problems if the requirement is unacceptable. Furthermore, a novel way to measure system affordability is proposed through the application of DFMEA. Lastly, the model is applied on CFR-14 Part-21 Subpart-G (FAA production organization requirements) to show the need for systematic improvement of aviation standards’ requirements of production organization.


identifier: Vietri, Joseph  (2021) Mere Love: The Theology of Need and Gift-Love in the Fiction of C.S. Lewis and J.R.R. Tolkien.  PhD thesis, Concordia University.

abstract: This thesis explores the role played by love in the works of C.S. Lewis and J.R.R. Tolkien. Need-love and Gift-love, as Lewis identifies them, are circular and interconnected. To reject one aspect would be to reject the entire human condition. More importantly, as creatures made in the image of a Creator, a Creator identified as Love, the ultimate Need and Gift-love can be found in God. Through a study of Lewis and Tolkien’s fiction, particularly The Chronicles of Narnia and The Lord of the Rings, this dissertation seeks to highlight the way in which love is portrayed as a central emotion for these authors. It will also highlight the role of free will, the ability to freely choose who to love. For both Lewis and Tolkien, it is this freedom which makes humans relational beings. Not only can we connect with one another, but it is only through this freedom that we can truly turn towards our Maker. Having established this important theological framework, emphasis will also be placed on the appropriation of stories like those of Cupid and Psyche and Orpheus and Eurydice to show how both thinkers attempted to connect their fiction to well-known stories. For the value and importance of stories are not only to entertain, but to also teach moral lessons. These lessons can be found scattered within the depictions of the worlds of Narnia and Middle-Earth.


identifier: Ali, Walaa Awad  (2021) Modeling and Optimization of Meat Supply Chain Transportation Network.  Masters thesis, Concordia University.

abstract:
In this thesis, a comprehensive meat supply chain (MSC) planning problem is studied. We consider live animals, chilled and frozen meat products as it is in the real case in most meat supply chain industries. A literature review is presented, and optimization-based method has been proposed in food supply industries. Two mathematical models are proposed considering comprehensive meat supply chain problems. The considered meat
supply chain problem was decomposed into two optimization sub-problems. The approach is to use two separate mathematical models for solving each sub-problem. The developed mathematical models are examined on a practical system in a meat supply chain company based in United Arab Emirates. The heuristic solution approach is introduced to solve the comprehensive meat supply chain optimization model developed. It can generate a good
solution for all tested example problems with much reduced computational time.


identifier: Guerrero Yáñez, Diana Carolina  (2021) Improving Thermal Comfort Conditions in K-12 Educational Buildings in Hot and Humid Climate.  Masters thesis, Concordia University.

abstract:
The school buildings in Colombia are designed and built based on geographical locations, available materials, and regional construction systems. However, external weather conditions and building design can have a significant impact on the indoor thermal conditions of classrooms and the thermal comfort of students, which affects the academic performance and productivity of students. This thesis investigates the thermal comfort performance of an educational building located in a hot and humid city in Colombia as a case study to evaluate the current design guidelines by the Ministry of Education and provide recommendations on improving the thermal conditions in educational buildings.

The selected school is a representative building of Colombian educational facilities, built under the architectural and structural guidelines established by the national government. This school is a concrete structure without mechanical cooling. It has features such as vertical louvers and light shelves to provide solar shading and cross ventilation. However, field observation discovered that 82% of the time, students experienced thermal and visual discomfort in elementary classrooms.

To investigate the potential causes and provide mitigation strategies, a whole building energy simulation is conducted. Design Builder is used to model the indoor thermal conditions of the school building based on existing building configuration under climatic conditions data collected. ASHRAE 55 adaptative model is used for the evaluation of thermal comfort. It is found that in these classrooms, 62% of the time, the thermal conditions are outside the acceptable range during the year. The effect of mitigation measures, i.e. occupancy, roof insulation, shading, and natural ventilation rates on indoor thermal conditions are investigated through simulations.

It is found that occupancy levels of 1.65m2/student and 2.85m2/student and natural ventilation from 4.0 to 6.0 ACH rate have a significant impact on the indoor temperature and relative humidity, and thus the thermal comfort. Passive design strategies are provided as general recommendations for optimizing the school building design to meet ASHRAE-55 requirements


identifier: Pourtaherian, Parnian  (2021) How do greenbelts affect urban sprawl in European cities?  Masters thesis, Concordia University.

abstract: As Europe takes continuous steps towards urbanization, many cities in this continent suffer from the negative repercussions caused by urban sprawl. Among the efforts adopted to overcome urban sprawl and its adverse impacts is the greenbelt policy that is highly popular in several European countries. However, the actual effectiveness of this growth management strategy was to be determined. Using a sample of 60 European cities, 30 of which have greenbelts, this study compares (1) changes in urban sprawl in a 9-year timeframe (2006-2015) between the cities with and without greenbelts, and (2) the level of sprawl between the cities with and without greenbelts in 2006 and 2015 separately, to investigate the performance of the greenbelts, applying the metrics of Weighted Urban Proliferation (WUP) and Weighted Sprawl per Capita (WSPC). The results showed that (1) greenbelts have been largely effective as an urban growth management strategy in slowing down urban sprawl; and in most cases, they also have helped reduce sprawl, (2) While urban sprawl decreased also in some cities without greenbelts, the relative decrease in urban sprawl was much stronger in cities with greenbelts, (3) Greenbelts were somewhat more beneficial in limiting urban sprawl in cities with larger population sizes, (4) The effectiveness of greenbelts was mainly due to the reduction of land uptake per person, i.e., through densification of the built-up areas. These findings can be employed as an important part of any de-sprawling strategies in future urban and regional planning, environmental monitoring, and formulating new scenarios as well as targets and limits to urban sprawl in support of more sustainable forms of urban development.


identifier: Fukushima, Kazuteru  (2022) Geometric Parameter Analysis of H-Darrieus Wind Turbines Based on 3D CFD.  Masters thesis, Concordia University.

abstract: The performance prediction of straight bladed vertical axis wind turbines is always of interest in the wind power generation industry. Computational Fluid Dynamic (CFD) is performed to explore the geometric design space, such as a turbine diameter, a blade length, and a wing section. The objective is to relate these design parameters to the maximum power coefficient of the H-type Darrieus vertical axis wind turbine. This analysis is based on data already available in the literature complemented by additional CFD runs to enhance the number of turbines investigated. By analyzing these results, two main geometric parameters, the blade thickness and large aspect ratio, are identified as the main parameters that affect the turbine’s power coefficient. This confirms the importance of the blade geometry in the performance of vertical axis wind turbines. Furthermore, some model equations are proposed to predict a power coefficient without any large simulations. The suggested model equations can roughly predict the power coefficients; moreover, it can quantify the effect of the blade thickness and the blade aspect ratio.


identifier: Guo, Jiaxun  (2021) Occupancy Estimation and Activity Recognition in Smart Buildings using Mixture-Based Predictive Distributions.  Masters thesis, Concordia University.

abstract:
Labeled data is a necessary part of modern computer science, such as machine learning and deep learning. In that context, large amount of labeled training data is required. However, collecting of labeled data as a crucial step is time consuming, error prone and often requires people involvement. On the other hand, imbalanced data is also a challenge for classification approaches. Most approaches simply predict the majority class in all cases.
In this work, we proposed several frameworks about mixture models based predictive distribution. In the case of small training data, predictive distribution is data-driven, which can take advantage of the existing training data at its maximum and don't need many labeled data. The flexibility and adaptability of Dirichlet family distribution as mixture models further improve classification ability of frameworks.
Generalized inverted Dirichlet (GID), inverted Dirichlet (ID) and generalized Dirichlet (GD) are used in this work with predictive distribution to do classification. GID-based predictive distribution has an obvious increase for activity recognition compared with the approach of global variational inference using small training data. ID-based predictive distribution with over-sampling is applied in occupancy estimation. More synthetic data are sampling for small classes. The total accuracy is improved in the end. An occupancy estimation framework is presented based on interactive learning and predictive distribution of GD. This framework can find the most informative unlabeled data and interact with users to get the true label. New labeled data are added in data store to further improve the performance of classification.


identifier: Rahemi, Hooman  (2021) Does brand personality affect consumer satisfaction? An application of text mining to restaurants.  Masters thesis, Concordia University.

abstract: Brand personality is a fundamental marketing construct that may be leveraged for competitive advantage. Researchers and market research firms often measure brand personality in consumer surveys. This approach is expensive, unscalable, and inefficient for tracking perceptions that constantly change over time. This research thus explores an alternative to the tracking of consumers’ brand personality perceptions. Online reviews offer textual data that can be extracted for marketing research. This research captures consumers’ brand personality and brand attribute (e.g., perceived price and perceived quality) perceptions of 40 restaurants, as reflected in 4,000 online reviews, using a deep-learning algorithm. It then investigates the effect of brand personality dimensions and brand attributes on consumer satisfaction. Findings indicate a strong and independent effect of sincere, exciting, and sophisticated brand personalities on consumer satisfaction while competence and ruggedness dimensions do not influence satisfaction. In addition, this study found support for an effect of additional brand attributes (i.e., perceived price, perceived quality, interpersonal service quality, psychic cost, time/effort cost) on satisfaction, with psychic cost perceptions having the greatest impact on consumer satisfaction.


identifier: Gao, Jie  (2021) Matching mechanisms for two-sided shared mobility systems.  PhD thesis, Concordia University.

abstract:
Shared mobility systems have gained significant attention in the last few decades due, in large part, to the rise of the service-based sharing economy. In this thesis, we study the matching mechanism design of two-sided shared mobility systems which include two distinct groups of users. Typical examples of such systems include ride-hailing platforms like Uber, ride-pooling platforms like Lyft Line, and community ride-sharing platforms like Zimride. These two-sided shared mobility systems can be modeled as two-sided markets, which need to be designed to efficiently allocate resources from the supply side of the market to the demand side of the market. Given its two-sided nature, the resource allocation problem in a two-sided market is essentially a matching problem.

The matching problems in two-sided markets present themselves in decentralized and dynamic environments. In a decentralized environment, participants from both sides possess asymmetric information and strategic behaviors. They may behave strategically to advance their own benefits rather than the system-level performance. Participants may also have their private matching preferences, which they may be reluctant to share due to privacy and ethical concerns. In addition, the dynamic nature of the shared mobility systems brings in contingencies to the matching problems in the forms of, for example, the uncertainty of customer demand and resource availability.

In this thesis, we propose matching mechanisms for shared mobility systems. Particularly, we address the challenges derived from the decentralized and dynamic environment of the two-sided shared mobility systems. The thesis is a compilation of four published or submitted journal papers. In these papers, we propose four matching mechanisms tackling various aspects of the matching mechanism design. We first present a price-based iterative double auction for dealing with asymmetric information between the two sides of the market and the strategic behaviors of self-interested agents. For settings where prices are predetermined by the market or cannot be changed frequently due to regulatory reasons, we propose a voting-based matching mechanism design. The mechanism is a distributed implementation of the simulated annealing meta-heuristic, which does not rely on a pricing scheme and preserves user privacy. In addition to decentralized matching mechanisms, we also propose dynamic matching mechanisms. Specifically, we propose a dispatch framework that integrates batched matching with data-driven proactive guidance for a Uber-like ride-hailing system to deal with the uncertainty of riders’ demand. By considering both drivers’ ride acceptance uncertainty and strategic behaviors, we finally propose a pricing mechanism that computes personalized payments for drivers to improve drivers' average acceptance rate in a ride-hailing system.


identifier: Tellos, Alison  (2021) Bricks or Studs: Exploring Irrelevant Details When Using LEGO® Bricks as Manipulatives.  Masters thesis, Concordia University.

abstract: The present study examined whether the studs on LEGO® bricks act as irrelevant details when solving fraction division problems and whether prior fractions knowledge played a role in children’s distraction and accuracy performance. Thirty-eight fifth- and sixth-grade students participated in the study. Participants were asked to complete a Fractions Test to assess their prior conceptual understanding of fractions. A median split was used to create a low prior knowledge group (n = 19) and a high prior knowledge group (n = 19). An instructional intervention showed students how to create fractions with LEGO® bricks and how to solve fraction division problem using measurement division. The results revealed that the studs did not distract the participants from solving fraction division problems with LEGO® bricks, regardless of prior knowledge. However, prior knowledge effects were found in accuracy performance, with low prior knowledge students generating less accurate solutions compared to high prior knowledge students. Participants also used both correct strategies and a variety of incorrect strategies to solve the fraction division problems. Particularly, children made errors in choosing the correct bricks to represent the dividend fractions, which, in part, resulted in having inaccurate solutions. The present study is relevant to teaching professionals as it provides new information about the ways children use LEGO® bricks to solve fraction division problems.


identifier: Baril, Daniel  (2021) Building Integrated Photovoltaic/Thermal Collector for Arctic Residential Applications.  Masters thesis, Concordia University.

abstract:
This thesis investigated the performance of an open loop air-based building integrated photovoltaic/thermal collector (BIPV/T) designed to preheat ERV supply air and to generate electricity. Energy Recovery Ventilators (ERV) have proven successful in cold climates, but in the extreme cold of the Arctic, frequent frosting and defrosting cycles reduce their effectiveness and increase the energy consumption. Thus, by integrating with BIPV/T which preheats the ventilation air, this problem can be reduced while also generating electricity. A finite difference model of the BIPV/T system integrated in a typical potential application was simulated in MATLAB using local weather data and indoor fresh air requirements to obtain system outputs. BIPV/T design parameters such as the tilt angle, and cavity depth were varied, with consideration of using nominal lumber sizes and ease of construction for improved implementation for Arctic residential applications. It was seen that the BIPV/T was able to increase the fresh air temperature supplied to the ERV up to 16°C and helped to reduce the defrosting time up to 7 hours per day. The 40m2 BIPV/T array also produced a considerable amount of electricity up to 33kWh/day and 7.5kWh/day of thermal energy was recovered.
Simulated electrical and thermal energy generated by the BIPV/T system are then compared with the measured energy usage data from a high-performance northern housing prototype located in Nunavik, Quebec. With this comparison the net energy usage is obtained along with the energy savings and was seen to reduce the annual electricity costs over 30% as well as approximately 5.5% of the total energy costs.


identifier: Srivastava, Gaurav  (2021) Post-Synthesis Growth of Lanthanide-doped Nanoparticles by Surface Modification.  Masters thesis, Concordia University.

abstract:
The growth mechanisms occurring during the synthesis of nanoparticles has been previously investigated by varying reaction conditions, such as reaction time, temperature, ligand and pH. The growth mechanisms of nanoparticles are reported only for high-temperature synthesis methods, by mechanisms such as Ostwald ripening and oriented attachment. Ostwald ripening involves the dissolution of small nanoparticles into monomers, with successive growth by ripening of the surface of bigger particles with these monomers. Oriented attachment occurs by the alignment of more particles following a preferential crystal orientation, and their successive attachment at the interface between particles by so-called necking. Among the previous studies, the mechanisms behind the growth of lanthanide-doped nanoparticles have been often reported, considering the interesting properties of these nanomaterials. Despite the thorough investigations reported in the literature, investigations into the potential for post-synthesis growth mechanisms in lanthanide-doped nanoparticles at room temperature has never been explored.

The presented research project aims to verify the possibility for tailoring the size and shape of lanthanide-doped nanoparticles at room temperature after their initial bottom-up synthesis at high temperature, in response to the present lack of knowledge in this field. The study of post-synthesis surface modification and growth at room temperature has to be carried out taking into consideration the same parameters that are usually varied in the reported investigations for synthesis at high temperatures, such as reaction time, ligand, pH, and temperature. For this project, the temperature is fixed at room temperature, and all the other parameters were varied. Oleate-capped nanoparticles were obtained by a co-precipitation reaction method. The potential for post-synthesis growth of two nanoparticle compositions were investigated (NaREF4 with RE = Y and Gd) to unveil the mechanism of the occurring surface phenomena. As a general protocol, the synthesized particles were subjected to ligand exchange from the as-synthesized oleate capping ligand to phosphonate ligands by first protonating the oleate ion by acidic treatment, followed by deprotonation of the phosphonate ligand and substitution on the nanoparticle surface in basic conditions. The pH was controlled by using two different bases (NaOH and KOH), and the occurrence of the ligand exchange was assessed by FT-IR spectroscopy. Modifications of nanoparticle size and shape during the post-synthesis growth were assessed by collecting aliquots at established time intervals and characterizing them via transmission electron microscopy and inductively coupled plasma mass spectrometry.

The results demonstrated that the ligand can facilitate changes in morphology and growth at room temperature in presence of different bases. NaREY4 nanoparticles do not show significant growth, but a necking behaviour was observed when NaOH was used as a base in the presence of the ligand, in which the nanoparticles orient themselves and connect within each other by forming “necks” between particles (growth by oriented attachment). On the other hand, NaREF4 nanoparticles showed a significant change in size distribution when KOH was used as a base in the presence of the ligand, by forming smaller particles compared to their initial size, which then condense onto the surface of bigger nanoparticles, proving that growth occurs by Ostwald ripening. These results show that it is quite possible to induce room-temperature growth of lanthanide-doped nanoparticles post synthesis by varying the cation of the base. The use of the ligand is crucial for preventing aggregation and inducing the coalescence of smaller nanoparticles into larger ones. To our knowledge, these observations in post-synthesis growth have been reported for the first time.


identifier: Babazadeh, Reza  (2021) Distance-Based Formation Control of Multi-Agent Systems.  PhD thesis, Concordia University.

abstract:
This Ph.D. dissertation studies the distance-based formation control of multi-agent systems. A new approach to the distance-based formation control problem is proposed in this thesis. We formulated distance-based formation in a nonlinear optimal control framework and used the state-dependent Riccati equation (SDRE) technique as the primary tool for solving the optimal control problem. In general, a distance-based formation can be undirected, where distance constraints between pairs of agents are actively controlled by both adjacent agents, or directed, where just one of the neighboring agents is responsible for maintaining the desired distance. This thesis presents both, undirected and directed formations, and provides extensive simulations to verify the theoretical results.

For undirected topologies, we studied the formation control problem where we showed that the proposed control law results in the global asymptotic stability of the closed-loop system under certain conditions. The formation tracking problem was studied, and the uniform ultimate boundedness of the solutions is rigorously proven. The proposed method guarantees collision avoidance among neighboring agents and prevents depletion of the agents' energy. In the directed distance-based formation control case, we developed a distributed, hierarchical control scheme for a particular class of directed graphs, namely directed triangulated and trilateral Laman graphs. The proposed controller ensures the global asymptotic stability of the desired formation. Rigorous stability analyses are carried out in all cases. Moreover, we addressed the flip-ambiguity issue by using the signed area and signed volume constraints. Additionally, we introduced a performance index for a formation mission that can indicate the controller's overall performance.

We also studied the distance-based formation control of nonlinear agents. We proposed a method that can guarantee asymptotic stability of the distance-based formation for a broad category of nonlinear systems. Furthermore, we studied a distance-based formation control of uncertain nonlinear agents. Based on the combination of integral sliding mode control (ISMC) theory with the SDRE method, we developed a robust optimal formation control scheme that guarantees asymptotic stability of the desired distance-based formation in the presence of bounded uncertainties. We have shown that the proposed controller can compensate for the effect of uncertainties in individual agents on the overall formation.


identifier: Pourganjalikhan, Ali  (2021) Adaptive Memory Management in Video Object Segmentation.  Masters thesis, Concordia University.

abstract:
Matching-based networks have achieved state-of-the-art performance for video object segmentation (VOS) tasks by storing every-k frames in an external memory bank for future inference. Storing the intermediate frames’ predictions provides the network with richer cues for segmenting an object in the current frame. However, the size of the memory bank gradually increases with the length of the video, which slows down inference speed and makes it impractical to handle arbitrary-length videos.
This thesis proposes an adaptive memory bank strategy for matching-based networks for semi-supervised video object segmentation (VOS) that can handle videos of arbitrary length by discarding obsolete features. Features are indexed based on their importance in the segmentation of the objects in previous frames. Based on the index, we discard unimportant features to accommodate new features. We present our experiments on DAVIS 2016, DAVIS 2017, and Youtube-VOS that
 demonstrate that our method outperforms state-of-the-art that employ first-and-latest strategy with fixed-sized memory banks and achieves comparable performance to the every-k strategy with increasing-sized memory banks. Furthermore, experiments show that our method increases inference speed by up to 80% over the very-k and 35% over first-and-latest strategies.
We further investigate memory banks’ attention during the training by proposing two regularizations and studying their effects on performance.


identifier: Yao, Yanxin  (2021) An information visualization platform for preprocessing of Electroencephalography (EEG) data from designing and learning.  Masters thesis, Concordia University.

abstract:
Electroencephalography (EEG) records the EEG signal with very high temporal resolution. The potential difference between the recording electrode and the reference electrode is used as the voltage, and the voltage changes over time to form the waveform of EEG, which can provide a basis not only for clinical diagnosis of mental illnesses but also for academic research to measure the mental effort of subjects. In the field of neuroscience, EEG technology is considered to be one of the most important technical tools for studying the brain, and the EEG signals collected need to be analyzed and processed in several complex steps to obtain the final desired results. The first step involved in the analysis and processing of EEG signals is the pre-processing of EEG signals. Currently, researchers have developed several open source toolkits for EEG signal processing and analysis, and these open source EEG toolkits have contributed to the development of the neuroscience field, but designing and developing a more complete EEG processing toolkit has been a challenging problem. Therefore, the focus of this thesis is to design and develop a user-centered information visualization platform that can preprocess EEG data.

In this study, the platform was designed using the analytical approach of environment-based design, and the platform was implemented using the python language. A series of validations of the platform's feasibility were performed based on data from loosely controlled EEG experiments at Concordia University's Design Lab. Based on the platform development results, a survey questionnaire and informed consent form for subjects were designed, and a survey on user satisfaction was conducted for the participating subjects.


identifier: Amini, Mohammad  (2021) Application of Machine Learning Algorithms to the Prediction of Water Main Deterioration.  Masters thesis, Concordia University.

abstract: Drinking water networks are among the essential infrastructure in cities worldwide. The failure of water mains jeopardizes this essential service and the safety of water users. However, across North America, the failure rate of older water mains has been increasing. The goal of this study is to compare the accuracy and applicability of machine learning algorithms to predict water main deterioration across Canadian water systems. In previous studies, different approaches were applied to only one or a few utilities. Nevertheless, it is valuable to compare results among various networks with different characteristics and levels of data collection. Accordingly, data was collected from thirteen Canadian water utilities, including Barrie, Calgary, Halifax, Kitchener, Markham, Region of Durham, Region of Waterloo, Saskatoon, St. John’s, Waterloo, Winnipeg, Victoria, and Vancouver. A variety of factors, including intrinsic, environmental, and operational, were used to develop more reliable predictions and assess the relative importance of each factor.  Random forest (RF), artificial neural networks (ANN), extreme gradient boosting (XGBOOST), and logistic regression (LR) were applied to predict the probability of failure. Furthermore, RF, ANN, XGBOOST, and ElasticNet regression models were employed to predict age at first failure and the current rate of failures. Results indicated the superiority of XGBOOST over other models in predicting the probability of failure and the current rate of failure. However, for age at first failure, RF performed better. When datasets were significantly imbalanced, the application of the Synthetic Minority Oversampling Technique (SMOTE) provided more accurate predictions. Because these models provide predictions for every pipe in the network, they can be mapped to facilitate the visualization of deterioration. While models created for one utility cannot be accurately applied to other utilities, the same machine learning algorithms can be quickly and effectively adapted to multiple utilities. Overall, these models support robust and data-driven asset management decision-making.


identifier: Joy, Alen ORCID: https://orcid.org/0000-0002-2423-4122 <https://orcid.org/0000-0002-2423-4122>  (2021) Estimating Spatially Varying BRDF of Complex Scenes Under Natural Illumination.  Masters thesis, Concordia University.

abstract:
In this thesis, we address the problem of estimating spatially varying bidirectional reflectance distribution function (SVBRDF) of complex outdoor scenes. We present two novel techniques for extracting the scene's reflectance maps. The reflectance maps can be used to re-render the scene in arbitrary lighting and viewing conditions.

In the first approach, we present a neural network trained on synthetic images of spheres representing a per-triangle face reflectance map. The trained neural network is then used to predict reflectance properties for each of the reflectance maps in the object. In the second approach, we propose an end-to-end process using a differentiable path tracer and formulate inverse rendering as a two-step optimization introducing a new multi-view gradient consistency loss. The BRDF is parameterized with three reflectance maps for diffuse, specular and roughness, optimized using the differentiable path tracer. The estimated  SVBRDF can then be used to relight the scene from novel viewpoints and illumination conditions.

We have extensively tested and reported the performance of our proposed techniques. Using multi-view images, camera properties, and geometry, we show that we can successfully estimate the reflectance properties of the scene. We also present our experiments on large-scale outdoor scenes, which demonstrate the effectiveness of the approaches.


identifier: Ghouchani, Rouzbeh  (2021) Studies in Mechanism Design.  PhD thesis, Concordia University.

title: Studies in Mechanism Design

abstract:
This thesis consists of three studies on mechanism design with and without monetary transfers. Following a brief introductory chapter, the second chapter contributes to the auction design literature and studies bidders with heterogeneous risk attitudes. The third and fourth chapters focus on matching couples to jobs without monetary transfers. The third chapter proposes a new method of preference aggregation for couples that enter the labour market together, and the fourth chapter proposes a new mechanism that accommodates couples in entry-level labour markets which relies on the preference aggregation studied in the third chapter.

In the second chapter I study the sale of a single indivisible good to two bidders with heterogeneous attitudes towards risk. Optimal auctions for risk neutral or risk averse bidders have been studied in the literature, but bidders are assumed to be either risk neutral or risk averse. My objective is to study the heterogeneity of bidders in terms of their risk attitude. In my model the valuations of the bidders are private information, however, one bidder is risk averse with a
publicly known degree of risk aversion, while the other bidder is risk neutral. I derive the revenue maximizing Bayesian incentive compatible auction in this environment.

The third chapter focuses on the aggregation of a couple’s preferences over their respective jobs when they enter a centralized labor market jointly, such as the market for assigning hospital residencies to medical students. Usually in such markets couples need to submit joint preferences over pairs of residency positions. Starting from two individual preference orderings over positions, we first study the Lexicographic and the Rank-Based Leximin rules, and then propose a family of aggregation rules, the k-Lexi-Pairing rules, and provide an axiomatic characterization of these rules. The parameter k indicates the degree of selfishness for one partner (and altruism for the other partner), with the least selfish Rank Based Leximin rule at one extreme and the most selfish Lexicographic rule at the other extreme. Since couples care about geographic proximity, we also identify a simple parametric family of preference aggregation rules, (k, t)-Lexi-Pairing rules, which also reflect the couple’s preference for togetherness.

In the fourth chapter we study centralized entry-level labour markets with couples and require couples to submit only (k, t)-Lexi-Pairing joint preferences as their input to the matching procedure. We introduce a new matching mechanism which takes advantage of the known preference structure of the joint preferences submitted by couples. This mechanisms resolves the cycles that typically arise in matching procedures with couples by working with the parameter t which indicates the degree of preference to be employed in the same geographic area for a couple. We also analyze the stability and efficiency properties of this new mechanism for couples’ markets. This is the first study that takes into account how couples form their paired preference orderings when participating in a centralized matching procedure, and the first mechanism which makes explicit use of the preference aggregation parameters that indicate the preferences of couples for togetherness.


identifier: Rimok, Gabrielle L.  (2021) The spatial ecology of climate influences species distributions: the case of North American amphibians.  Masters thesis, Concordia University.

abstract: Species distributions are largely determined by three main drivers: abiotic environmental conditions, dispersal, and biotic interactions. Because abiotic environmental conditions determine habitat suitability, they also have direct implications on the capacity of species to disperse and how species interact with one another in space. However, it is specifically the variability in abiotic environmental conditions (i.e., environmental heterogeneity) and how they are spatially structured (i.e., environmental spatial autocorrelation - ESA) that determines whether or not a habitat, or even a landscape, is environmentally suitable for species establishment. Environmental heterogeneity itself is spatially structured; where environmental conditions/features that are closer together in space tend to be more similar than those farther apart. As such, the spatial structure of environmental features (i.e., ESA) mimics dispersal networks because spatial patterns in environmental heterogeneity affect the strategies and energetic costs (and their associated fitness consequences) involved in movement and dispersal among patches. At broad spatial scales, species distributions are shaped by environmental conditions, namely, those of climate. Climatic conditions thus also impose important physiological and life history constraints on species and in accordance with environmental features, are also often heterogeneous and spatially structured. Yet, how they affect and contribute to species distributions remains unknown. Here, we use species distribution models (SDMs) in a novel framework in which we demonstrate for the first time, the influence of climate heterogeneity (within and between patches) and climate spatial structure on species distributions. We evaluated six different SDMs testing both the individual and combined effects of climate variables (i.e., between and within-patch climate heterogeneity and climate spatial structure) on species distributions, using 301 North American amphibian species as a case study. Our results demonstrate that a model using climate spatial structure as a predictor alone explained species distributions better than any other model in the majority of species. Although a model including both climate heterogeneity (within and between-patch) and climate spatial structure as predictors was only the best model for a handful of species, we provide critical evidence that there is added value in considering climate spatial structure when fitting different SDMs for the same species. Most importantly, we demonstrate that climate spatial structure and heterogeneity are important mechanisms driving species distributions in North America.


identifier: Nouhi, Niloufarsadat  (2021) Modeling of dielectric elastomer actuators with a conical shape.  Masters thesis, Concordia University.

abstract:
Dielectric elastomer actuators (DEAs) have received a lot of attention in the last decade due to their outstanding actuation strain, high energy density, high degree of freedom, electromechanical coupling and low price. However, modelling of dielectric elastomer actuators is complicated because of time-dependent viscoelasticity, complex geometry, electromechanical coupling and material nonlinearity. For these reasons, just a few research results focusing on modeling of the DEAs have been published.

In this research, taking into account the influence of viscoelasticity, we present a physical and phenomenal based model to characterize the behaviour of a conical DEA made of polydimethylsiloxane. The nonequilibrium thermodynamic framework is used to characterize the mechanical coupling of DEA. Also, free energy and viscoelastic characteristics of DEA are described using the Gent model and the generalized Kelvin model, respectively.

The differential evolution approach is used to find the model parameters based on the experimental data. The model’s validity and generalization are proved by comparing experimental results with model predictions, for both different driving input frequencies and amplitudes. The experimental results demonstrate a high level of agreement with the developed model.


identifier: LOUDEGUI DJIMDOU, Magloire  (2021) Inference Procedures for Copula-Based Models of Bivariate Dependence.  PhD thesis, Concordia University.

abstract:
In this thesis, we develop inference procedures for copula-based models of bivariate dependence. We first investigate the distribution of Kendall’s functions for joint survivors since Kendall’s functions
are important for identifying Archimedean copula models. We then provide two estimators for the generator of an Archimedean copula. We also propose a plug-in estimator for Kendall’s tau and a
maximum pseudo-likelihood estimation for the copula model parameters in cases where the survival times are subject to bivariate random censoring. These tests are based on the usual (univariate) Kaplan-Meier estimator and a recently proposed estimator for the bivariate case.


identifier: Fattori, Laura ORCID: https://orcid.org/0000-0003-4800-3334 <https://orcid.org/0000-0003-4800-3334>  (2021) Narratives of Post-Conflict: Representation of Violence in Colombian Contemporary Cinema.  Masters thesis, Concordia University.

abstract:
This research project is centered on the tight relationship between the articulation of social imaginaries and the representation of the Colombian internal conflict in national cinema. The Colombian peace-process situation currently encompasses over 50 years of conflict and a continuous search – through different manifestations of memory and intense use of media – of a new cultural identity. The narrative mechanisms of representation adopted by mass media are a fundamental part of the reconciliation process as they are a crucial form of collective memory and, simultaneously, the exercise of power that takes the shape of an official cultural memory. The objective of this study is to identify the narrative structures and conjunctions through which violence is assimilated, (re)presented, filed and inserted in the field of cultural memory by Colombian mass media after the signing of the peace treaty in
2016.


identifier: Markousian, Artin  (2021) Analysis of the Impact of External Factors on Human Errors in a Typical Aero-Engine Plant.  Masters thesis, Concordia University.

abstract:
Aero-engine assembly activities type and volume limit the manufacturers’ ability to automate all processes; thus, the assembly of engines and many supporting processes heavily rely on the operators’ skills. Human operators show significant variability in their performance, which is usually referred to as human errors. Such errors are identified as the Escape to perform an action within the safe operating limits and often lead to quality defects. With a particular focus on one of the engine families (Group A engines) of a typical aero-engine plant,  the first objective of our analysis revolves around identifying the interruptions and distractions incurred during the engine assembly process that can potentially lead to quality defects induced by human errors. Based on the analysis of the quality reports, the group A engines produced in this facility have a higher percentage of quality defects as compared with the average quality defect rates of other engine groups within the company.
It is noteworthy that quality reports suffer from a lack of information regarding the relationship between reported quality defects and human errors. In other words, the data extracted from these reports cannot be directly used to identify sources of such errors. Therefore, two main data collection methods, namely observations through site visits and interviews, are alternatively used. Afterward, a combination of various Lean Manufacturing and quality engineering approaches, namely Value Stream Mapping (VSM), cost of quality escapes, cause-and-effect diagram, Escape Mode and Effect Analysis (FMEA), and Analytic Hierarchy Process (AHP) are explored to provide a prioritized list of external root causes that can potentially lead to human errors at the assembly facility.
The current Value Stream Map analysis results, along with our observations from the assembly facility and the conducted interviews, are presented as twenty-five sources of interruptions and distractions that could be the root causes of quality defects (due to human errors). The results are summarized in a fishbone diagram with six major categories; material, communication, manpower, methods, environment, and movement. Afterward, by using FMEA and AHP methods, those twenty-five sources of interruptions and distractions are prioritized. Results revealed that the leading root causes include: Reworks/QNs/Andons; insufficient root cause analysis of frequent problems/breakdowns; frequent changes in production priorities; informal information flow and inaccuracy of transferred information; distractive people or activities in assembly area; slow and complicated procedure of technical support; lack of inspection or quality control between sub-assemblies; and inefficient order of executing assembly flow sequences.
The second goal of this study is to propose Lean manufacturing and quality engineering solutions in order to eliminate the root causes identified in the first objective.  The proposed solutions rely on: i) improving the quality control system via enhancing the documentation methods for recording quality Escape; in addition to ii) developing a Split Mixed-Model VSM for the engine assembly facility. The first solution, in particular,  aim to use statistical control tools and root cause analysis to reduce assembly Escapes caused by human errors.  Implementing a Split Mixed-Model VSM, on the other hand, contributes to a smooth flow of products while systematically eliminating sources of interruptive and distractive issues during the assembly process. More precisely, it mainly targets the elimination of quality defects caused by stoppages due to shortage of components/sub-assemblies, late delivery of materials, delivered materials with quality defects, and insufficient assemblers or support teams. In other words, the proposed split mixed-model value stream is expected to favourably affect the quality and performance of the process in this plant. When developing this VSM, the order of doing assembly instructions is rearranged; the new TAKT time for each engine model in the Group A engine family is calculated, and a new layout for the assembly facility is accordingly provided.


identifier: Mousavi, Seyed Amirreza  (2021) A Distributed False Data Injection Cyber-Attack Detection in Discrete-Time Nonlinear Multi-Agent Systems Using Neural Networks.  Masters thesis, Concordia University.

format: text text

abstract:
In this thesis we study a detection method for the false data injection (FDI)  attack class on discrete-time nonlinear multi-agent systems. The thesis considers and models three types of FDI attacks on multi-agent communication channels, including sensor channel, actuator channel, and neighbouring channel. To estimate the dynamics nonlinearity of each agent, we exploit a radial basis function neural network (RBFNN). We consider a leader-follower multi-agent system, where the communication between agents is modeled with an undirected graph. We proposed the weight tuning law for the RBFNN and introduced an NN-based distributed control law for each agent. The objective of each agent is to follow the leader and maintain the desired formation along the trajectory.  We used the Lyapunov stability analysis to prove the uniform ultimate boundedness (UUB) of the formation error and neural network (NN) weights matrix and show that the multi-agent system reaches the desired w while following the leader.

Moreover, we proposed a distributed attack detection method to detect the FDI attack on each agent's sensor, actuator, and neighbouring communication channel. We designed an observer to estimate the state of each agent and used its estimation to form the residual signal for each agent. Using Lyapunov stability analysis, we show that when the system is reached its desired formation, the residual signal is UUB. We obtained abound for the residual signal and considered the bound as the attack detection threshold.  We also provided the attack detectability condition for each agent.

The simulation results in MATLAB and Coppeliasim simulation environment are provided to demonstrate the performance of the detection methodology, proposed distributed control law, and neural network nonlinearity estimator, including three examples.


identifier: Tajrine, Dima  (2021) “See, That’s a Letter": Online Instruction to Promote Educators’ Print Referencing.  Masters thesis, Concordia University.

abstract: Emergent literacy refers to pre-literacy skills that develop in the preschool years through informal activities such as storybook reading. It includes phonological awareness, oral language, and print knowledge. Print referencing is an evidence-based strategy that promotes print knowledge during storybook reading. Educators have an important role to play in the development of print knowledge. However, they pay very little attention to print during storybook reading. This study examined the effect of a multi-faceted online instruction (with synchronous and asynchronous components) on the knowledge of emergent literacy and print knowledge and the application of print referencing by preservice educators during storybook reading. Specifically, 25 undergraduate student participants were assigned to two groups, the experimental print referencing (PR) group (n = 12) and the comparison Questions (Q) group (n = 13). Participants in the PR group received instruction on print referencing whereas the Q group received instruction on asking inferential questions, to foster another component of emergent literacy. A questionnaire evaluated participants knowledge of emergent literacy and print referencing and a role play measured their use of the learned strategies. Answers to open-ended questions about the instruction were also collected through learning logs which were filled as part of class work. Mixed ANOVA showed that both groups increased in emergent literacy and print referencing/print awareness knowledge. However, as expected, only the PR group transferred their print awareness/print referencing knowledge to practice as demonstrated by the role play. Qualitative data through the learning logs showed positive feedback towards the online instruction and print referencing as an important and useful tool for preservice educators to have.


identifier: Salehabadi, Nooshin  (2021) Maintenance Decision Support Procedures Based on Machine Learning.  Masters thesis, Concordia University.

abstract: In a competitive production environment, a manufacturing company must have plans to improve production performance. To improve production performances depends on various issues such as production efficiency and machine availability. Various preventive maintenance procedures have been developed for efficiently maintaining and repairing machines and equipment in a manufacturing system to maximize machine availability and its readiness for production.  In recent years, Artificial Intelligence (AI) technology has been applied in developing maintenance procedures in industries utilizing advanced information technology such as the Internet of Things (IoT). This thesis presents a machine learning model to predict machine failures and maintenance requirements for certain industrial machine tools. Machine learning methods enable manufacturing systems to make smart decisions through communications with humans and machines using sensors. A Logistic regression model is developed in this study to predict machine failures for the purposes of avoiding machine breakdowns and improving system performance. The supervised classification method was incorporated in the developed prediction model. The developed model is tested verified with realistic machine maintenance data. Computational experiments are conducted with results analyzed.


identifier: Daradkeh, Tariq ORCID: https://orcid.org/0000-0002-1294-7117 <https://orcid.org/0000-0002-1294-7117>  (2021) An Optimized Deep Machine Learning and Micro-Services Architecture based Proactive Elastic Cloud Framework.  PhD thesis, Concordia University.

abstract:
To achieve elasticity in cloud environment a holistic solution must be considered that measures all running applications and resources performance, including its cloud management system. Cloud resources and applications are continuously changing in their capacity and behavior, which implies a dynamic change in the cloud management system architecture and characteristics. The new era of application modeling is to decouple its components and make them as standalone cooperated modules following micro-service pattern architecture. This design gives an application a fast adaptation agility to change in requirements by customizing the application operation modules to match new tasks. The proposed elastic framework is achieved using multiple tasks as a sequence of steps. First, cloud resources monitoring, and workload changes are tracked. Second, workloads clustering using custom K-means method is used to categorize unlabeled workload sets. Third, workload demands, and datacenter configuration are predicted, classified, and labeled using deep machine learning techniques. Fourth, resources are scaled and scheduled based on workload characteristics and scaling dimension conditions. Fifth, a micro-service pattern based elastic framework is implemented for dynamic resources management and operation.

First task, monitoring system must provide needed information to cloud manager to describe cloud dynamic state by reading cloud-generated logs and sending them to cloud manager. Log updates should be accurate, instantaneous, and sent with minimum time delay. Data sources vary from low to high level of cloud infrastructure resources, or they can be generated from workload demands. Logs are used to discover cloud system status, which is input for future actions in cloud management resources orchestration. Point Estimator (PE) log tracker is proposed that can dynamically adapt to the type of workload providing accurate fresh logs values to cloud manager with minimum number of data transactions.
Second task, dynamic K-Means clustering using kernel density estimator is proposed to analyze and characterize both workloads and datacenter configurations. This method enhances K-Means clustering by automatically determining optimum number of classes and finding the mean centroids for the clusters. In addition, it improves the accuracy and the time complexity of standard K-Means clustering model, by best correlating between clustering attributes using statistical correlation methods.

Third task, cloud workload prediction is a very critical task for elastic scaling, because cloud manager decides what configuration sequence is to be considered for resource provisioning. Predicting workload demands to optimize datacenter configuration, such that increasing/decreasing datacenter resources provides an accurate and efficient configuration. Three methods of deep machine learning (namely NN, CNN and LSTM) are used and compared with analytical approach to model workload and datacenter actions. Analytical model is used as predictor to evaluate and test optimization solution set and find the best configuration and scaling actions before applying it on the real datacenter. Deep machine learning together with analytical approach is used to find the best prediction values of workload demands and evaluate the scaling and resources capacity required to be provisioned. Deep machine learning is used to find optimal configuration and to solve the elasticity scaling boundaries values. %Matching the demand guarantees Service Level Agreement (SLA) conditions and Quality of Service (QoS) performance.

Fourth task, resources scaling and scheduling in cloud elasticity involves timely provisioning and de-provisioning of computing resources and adjusting resources size to meet the dynamic workload demand. This requires fast and accurate resource scaling methods at minimum cost (e.g. pay as you go) that match with workload demands. Two dynamic changing parameters must be defined in an elastic model, the workload resource demand classes, and the data center resource reconfiguration classes. These parameters are not labeled for cloud management system while data center logs are being captured. A deep machine learning method is used to label datacenter configuration.
Fifth task, micro-service pattern architecture with open standard API is used to integrate between all elastic cloud framework components. Full stack micro-service based elastic cloud management system is implemented considering elastic scaling and management requirements of all resources. The model focuses on elasticity scaling performance by analyzing cloud micro-service management modules in different aspects: interactions, end to end delay, and communication. It also focuses on optimizing decoupling of system components and optimizing orchestration scheduling for elastic scaling.


identifier: Wang, Jiayi  (2021) The Persuasiveness of Celebrity versus Social Media Influencer Endorsements as a Function of Product Type and the Mediating Role of Endorser-Product Perceived Fit.  Masters thesis, Concordia University.

abstract: The endorsement marketing strategy has proven to be a very effective way to forge strong relationships with consumers, as well as stimulate stronger brand and product preferences and purchase intentions. There are two main types of endorsers that are commonly used in practice: celebrities and social media influencers. While both have been shown to have substantial persuasive power, there exist nuances in when and how each type of endorser impacts consumer outcomes. This paper aims to compare the effects of celebrity versus social media influencer endorsers on customers’ attitude toward promoted products and their purchase intentions, taking into consideration the moderating role of product type (everyday product vs. luxury product) and examining the mediating role of perceived endorser-product fit. Two online surveys (N= 590, N=572) were conducted and the results showed that 1) compared to celebrity endorsers, social media influencers have a stronger positive effect on product attitude and purchase intention for everyday products, but only among male customers, 2) this relationship was mediated by perceptions of endorser-product fit, and 3) celebrity and influencer endorsers did not differentially impact consumer attitude and purchase intention for luxury products. Unfortunately, we did not find any significant results for the female sample.


identifier: Bataineh, Ahmed  (2021) Toward Monetizing Data for AI-driven Services on Cloud Computing and Blockchain.  PhD thesis, Concordia Institute for Information Systems Engineering.

abstract:
AI-driven services and data collecting-based applications are these days the talk of the town in the field of computer science and beyond. This is often obvious since one can effortlessly take note, by looking around, how this field has ended up fundamental in our day to day lives beginning from business intelligence and market analysis down to virtual personal assistants (e.g., Siri, Google Now) and social media analysis (e.g., people you may know on Facebook). However, the research communities anticipate a turn down in the revolution of AI-driven services and data collecting-based applications due to the deficiency within the accessibility of huge data that ought to be collected or (pre-)trained using machine learning technologies. Mainly, collecting and integrating the big complementary data scattered across foundations and countries entails high costs and management challenges associated with finding and getting on board multiple data providers. In this thesis, we tackle this problem by designing a data market platform on top of the cloud computing technology. The data market platform helps the data providers and data consumers find and meet each other, while the cloud technology provides the required computing resources to execute computational tasks associated with data processing. This thesis starts by searching and investigating the most efficient business theories to model the data market platform. As a result of our search, the two-sided market theory has been proposed as a successful underlying model to design the data market platform.

The two-sided market theory has been improved in this thesis to handle challenges associated with considering data as an economic good on the one hand, and reshaping the business of the cloud computing to act as a data market platform on the other hand. Mainly, we introduce a novel game theoretical model (Two-sided game), which consists of a mix of cooperative and competitive strategies. The players of the game are the big data providers, cloud computing platform, and data consumers. The strategies of the players are modeled using the two-sided market theory that takes into consideration the network effects (externalities) among involved parties. The externalities refer to the mutual impact of the number of data providers and data consumers on each other. The objective of this game is to enable the cloud to be an active platform that can help big data service providers reach a wider set of customers and cloud users (i.e., data consumers) to be exposed to a larger and richer variety of data to run their data analytic tasks. The proposed game has been improved further to deliver complementary data services among multiple data providers over a cloud intermediary platform. More specifically, we formalize the problem as an extended two-sided market model by courting on one side some influential data providers in order to attract other data providers on the same side to form a bundling of data services. The final game aims to dynamically distribute the cloud computing resources among computational tasks of data providers to maximize the social welfare of all involved parties. The game has been supported by a mechanism to handle potential undesired behaviours such as the greedy and irrationality behaviours of involved parties. The game also provides a clear pricing mechanism that estimates the monetary value of data considering the actual need of the data consumers.

The thesis ends up by involving the blockchain technology in the process of monetizing data. The blockchain technology has recently proved to be an efficient solution for guaranteeing the security of data transactions in data trading scenarios. The benefits of the blockchain in this domain have been shown to span over several crucial security and privacy aspects such as verifying the identities of data providers, detecting and preventing malicious data consumers, and regulating the trust relationships between the data trading parties. However, the cost and economic aspects of using this solution such as the pricing of the mining process have not been addressed yet. In fact, using the blockchain entails high operational costs and puts both the data providers and miners in a continuous dilemma between delivering high-quality security services and adding supplementary costs. In addition, the mining leader requires an efficient mechanism to select the tasks from the mining pool and determine the needed computational resources for each particular task in order to maximize its payoff. Motivated by these two points, we propose in this thesis a novel game theoretical model based on the two-sided market approach that helps both the data providers and miners determine the monetary reward and computational resources, respectively.


identifier: Dib, Mirabelle ORCID: https://orcid.org/0000-0003-3134-1230 <https://orcid.org/0000-0003-3134-1230>  (2021) On Leveraging Next-Generation Deep Learning Techniques for IoT Malware Classification, Family Attribution and Lineage Analysis.  Masters thesis, Concordia University.

abstract:
Recent years have witnessed the emergence of new and more sophisticated malware targeting insecure Internet of Things (IoT) devices, as part of orchestrated large-scale botnets. Moreover, the
public release of the source code of popular malware families such as Mirai [1] has spawned diverse variants, making it harder to disambiguate their ownership, lineage, and correct label. Such a rapidly
evolving landscape makes it also harder to deploy and generalize effective learning models against retired, updated, and/or new threat campaigns. To mitigate such threat, there is an utmost need for effective IoT malware detection, classification and family attribution, which provide essential steps towards initiating attack mitigation/prevention countermeasures, as well as understanding the evolutionary trajectories and tangled relationships of IoT malware. This is particularly challenging
due to the lack of fine-grained empirical data about IoT malware, the diverse architectures of IoT-targeted devices, and the massive code reuse between IoT malware families.

To address these challenges, in this thesis, we leverage the general lack of obfuscation in IoT malware to extract and combine static features from multi-modal views of the executable binaries (e.g., images, strings, assembly instructions), along with Deep Learning (DL) architectures for effective IoT malware classification and family attribution. Additionally, we aim to address concept drift and the limitations of inter-family classification due to the evolutionary nature of IoT malware, by detecting in-class evolving IoT malware variants and interpreting the meaning behind their mutations. To this end, we perform the following to achieve our objectives:

First, we analyze 70,000 IoT malware samples collected by a specialized IoT honeypot and popular malware repositories in the past 3 years. Consequently, we utilize features extracted from strings- and image-based representations of IoT malware to implement a multi-level DL architecture that fuses the learned features from each sub-component (i.e, images, strings) through a neural network classifier. Our in-depth experiments with four prominent IoT malware families highlight
the significant accuracy of the proposed approach (99.78%), which outperforms conventional single-level classifiers, by relying on different representations of the target IoT malware binaries that do not
require expensive feature extraction. Additionally, we utilize our IoT-tailored approach for labeling unknown malware samples, while identifying new malware strains.

Second, we seek to identify when the classifier shows signs of aging, by which it fails to effectively recognize new variants and adapt to potential changes in the data. Thus, we introduce a robust and effective method that uses contrastive learning and attentive Transformer models to learn and compare semantically meaningful representations of IoT malware binaries and codes without the need for expensive target labels. We find that the evolution of IoT binaries can be used as an augmentation strategy to learn effective representations to contrast (dis)similar variant pairs. We discuss the impact and findings of our analysis and present several evaluation studies to highlight the tangled relationships of IoT malware, as well as the efficiency of our contrastively learned fine-grained feature vectors in preserving semantics and reducing out-of-vocabulary size in cross-architecture IoT malware binaries.

We conclude this thesis by summarizing our findings and discussing research gaps that lay the way for future work.


identifier: Sadhukhan, Priyam  (2021) Proximal Policy Optimization for Formation Control and Obstacle Avoidance in Multi-Agent Systems.  Masters thesis, Concordia University.

abstract: In this thesis, deep reinforcement learning (DRL) is used for intelligent formation control and obstacle avoidance in multi-agent systems through reward shaping. The objective of this work is to study the application of proximal policy optimization (PPO) algorithm for maneuvering a formation of agents around obstacles. Each agent in the multi-agent system is modeled as a holonomic second-order integrator and the formation is allowed to shrink while maintaining its shape in order to navigate around obstacles and take the geometric centroid of the formation towards the goal. We investigated both angle-based rewards and bearing-based rewards. Experiments were carried out in a two-dimensional simulation environment with different number of agents and multiple obstacles between the formation and the goal. Curriculum learning was used to train the agents in environments with different initializations for the agents, goal and obstacles. Simulation results show the effectiveness of the different reward schemes.


identifier: Haddad, Mark ORCID: https://orcid.org/0000-0003-3064-2381 <https://orcid.org/0000-0003-3064-2381>  (2021) An Instance-Based Learning Statistical Framework for One-Shot and Few-Shot Human Action Recognition.  Masters thesis, Concordia University.

abstract: Along with the exponential growth of online video creation platforms such as TikTok and Instagram, state of the art research involving quick and effective human action/gesture recognition remains crucial. This thesis presents an instance-based statistical framework which addresses the challenge of classifying short human action video clips, using a domain-specific feature design approach, capable of performing significantly well using as little as one training example per action (one-shot learning). The method is based on Gunner Farneback's dense optical flow (GF-OF) estimation strategy, Gaussian mixture models, and information divergence. We first aim to obtain accurate representations of the human movements/actions by clustering the results given by GF-OF using K-means method of vector quantization. We then proceed by representing the result of one instance of each action by a Gaussian mixture model. Furthermore, using Kullback-Leibler divergence (KL-divergence), we estimate the information divergences in an attempt to find similarities between the trained actions and the ones in the test videos. Classification is then done by matching each test video to the trained action with the highest similarity (a.k.a lowest KL-divergence value). We have performed experiments on the KTH and Weizmann Human Action datasets using One-Shot and K-Shot learning approaches, and the results reveal the discriminative nature of our proposed methodology in comparison with state-of-the-art techniques.


identifier: Vineberg, Rebecca  (2021) The Digital Age: Exploring Age and Technology amid the COVID-19 Pandemic.  Masters thesis, Concordia University.

abstract:
The COVID-19 pandemic challenged organizations to accelerate and transform their technology landscape in order to support business continuity by introducing aspects such as remote work and process automation. Central to research on technology in the workplace is the concept of age, and how older workers adapt to technology compared to younger peers. The extant literature on age and technology in the workplace posits that older workers are less willing and able to adapt to and adopt technology, and tends to focus on factors such as neurological and physiological aging as well as motivational factors related to using technology. The following quantitative and qualitative research proposes a model of aging and technology in the workplace from a stereotype-threat perspective, and hypothesizes that subjective age can play a role in older workers’ self-perceptions of their ability to use technology. Through time-lagged research that looked at both quantitative and qualitative measures, we found support for the role of cognitive age in predicting computer anxiety and computer self-efficacy better than chronological age. In addition, we found evidence that comparative age plays a role as well, such that workers with similarly aged colleagues tend to evaluate their experiences using technology differently than workers with younger colleagues. Overall, the research supports that social and environmental factors are salient considerations that must be understood more clearly in order to truly understand the relationship between age and technology adoption.

Keywords: Age, technology, stereotype, computer anxiety, computer self-efficacy


identifier: Matskiv, Jacqueline  (2021) Biopolitics and Affective Life: Investigating the Digital Ordinary.  Masters thesis, Concordia University.

abstract:
This thesis contends that the central problem of affect theory––the body’s potentiality to affect and be affected in return––is in fact a distinctly biopolitical one. Beginning from this claim, this thesis explores how biopower targets and manages the affective capacities of its subjects, and how digital media inscribe this dynamic and reflect it back to us. Specifically, it poses the following: what might a feminist inquiry into the affective logic of contemporary biopolitics, as made manifest in the digital ordinary, tell us about life under contemporary configurations of power? In posing and responding to this question, I mobilize a biopolitical paradigm to illuminate the power dynamics implicit in everyday affective life in the digital world. More specifically, I argue that social media platforms could only emerge in the context of neoliberal biopolitics, and that they can thus be mined for evidence of its mechanisms, logics and motives. Methodologically, I demonstrate that this does not require insider expertise of the workings of algorithms or the internal operations of social media companies, but rather a patient, critical attentiveness to our ordinary affective experiences online.
Keywords: biopolitics, affect, social media platforms, digital ordinary


identifier: Jodavi, Mehran  (2021) Accurate Program Element Tracking in Commit History.  Masters thesis, Concordia University.

abstract:
Tracking program elements in the commit history of a project is essential for supporting various software maintenance, comprehension and evolution tasks.
	Accuracy is of paramount importance for the adoption of program element tracking tools by developers and researchers.
	To this end, we propose CodeTracker, a refactoring-aware tool that can generate the commit change history for method and variable declarations with very high accuracy.
	More specifically, CodeTracker has 99.9\% precision and recall in method tracking, surpassing the previous state-of-the-art tool, CodeShovel, with a comparable execution time.
	CodeTracker is the first tool of its kind that can track the change history of variables with 96.7\% precision and 95.5\% recall.
	To evaluate its accuracy in variable tracking, we extended the oracle created by Grund et al. for the evaluation of CodeShovel, with the complete change history of all 1345 variables and parameters declared in the 200 methods comprising the Grund et al. oracle.
	We make our tool and extended oracle publicly available to enable the replication of our experiments and facilitate future research on program element tracking techniques.


identifier: Behnam, Shobiri  (2021) CDNs' Dark Side: Identifying Security Problems in CDN-to-Origin Connections.  Masters thesis, Concordia University.

abstract:
Content Delivery Networks (CDNs) play a vital role in today's Internet ecosystem. To reduce the latency of loading a website's content, CDNs deploy edge servers in different geographic locations. CDN providers also offer important security features including protection against DoS attacks, Web Application Firewalls (WAF), and recently, issuing and managing certificates for their customers. Many popular websites use CDNs to benefit from both the security and performance advantages.

For HTTPS websites, TLS security choices may differ in the connections between end-users and a CDN (front-end or user-to-CDN), and between the CDN and the origin server (back-end or CDN-to-Origin). Modern browsers can stop/warn users if weak or insecure TLS/HTTPS options are used in the front-end connections. However, such problems in the back-end connections are not visible to browsers or end-users, and lead to serious security issues.

In this thesis, we primarily analyze TLS/HTTPS security issues in the back-end communication; such issues include inadequate certificate validation and support for vulnerable TLS configurations. We develop a test framework and investigate the back-end connection of 14 leading CDNs (including Cloudflare, Microsoft Azure, Amazon, and Fastly), where we could create an account. Surprisingly, for all the 14 CDNs, we found that the back-end TLS connections are vulnerable to security issues prevented/warned by modern browsers; examples include failing to validate the origin server's certificate, and using insecure cipher suites such as RC4, MD5, SHA-1, and even allowing plain HTTP connections to the origin. We also identified 168,795 websites in the Alexa top million that are potentially vulnerable to Man-in-the-Middle (MitM) a attacks in their back-end connections regardless of the origin/CDN configurations chosen by the origin owner.


identifier: Meng, Linghao  (2021) Antecedent Factors and their Influence on the Young Entrepreneur’s Future Sustainable Intentions.  Masters thesis, Concordia University.

abstract: This exploratory study aims to understand the antecedent factors (sustainable/entrepreneurial orientations, self-determination motivation, and contextualities) that may influence a future young entrepreneur’s intentions in developing sustainable enterprise practices (SEP). Adopting humanistic values from a service-dominant logic framework, we evaluate how a future young entrepreneur’s mindset can be shaped in adopting sustainable enterprise best practices on ethical and moral altruistic decision-making. Based on the self-determination theory (SDT), we looked at the autonomous and self-regulatory motivation factors as key influencers on the individual decision-making process, and on how he/she weights contextual complexities in deciding to pursue doing good for the wellbeing of the organization and the community or preferring opportunistic self-interested rewarding goals. Combining online surveys and in-depth interviews in Canada and in China, it was found that intrinsic motivation factors influence the development of sustainable entrepreneurial orientations (SO) and sustainable enterprise practices (SEP). Although contextual and cultural factors moderate the effects of intrinsic motivations, one should capitalize on learned behaviors. As suggested through the in-depth interviews with experienced entrepreneurs, future young decision-makers should be expressly taught about ethical, social, and environmental issues to develop SEP intentions and potential future sustainable entrepreneurial behaviors.


identifier: Murray, Kristopher  (2021) Making it Big: Street Art Muralism in a Post-Political World.  PhD thesis, Concordia University.

abstract: Making it Big is an ethnographic exploration of the critical role that graffiti and street artists can play in resisting neoliberal attempts to pacify radical modes of artistic practice in North American cities today. Over the last decade, street art muralism has increasingly been identified as a key component in reshaping urban infrastructures and economies, namely though the development of arts districts and the organization of urban or mural arts festivals. It has also been mobilized to confront social injustices and raise public awareness to environmental and global issues. Influenced by graffiti and street art, street art muralism is argued as being a distinct form of public art, heroic to monumental in scale, and produced in public settings with consent. The shift towards professional and institutionally managed street art mural projects and programs demands a closer and critical evaluation of equality and content in place and space, and the democratization of arts in the city. As commercial and government interests grow urban arts infrastructures using street art mural-based tourism strategies, they are met with either support or resistance from purists and muralists in the graffiti and street art communities. On the one hand, purists argue that the street art muralism threatens to supplant graffiti culture and informal systems of aesthetic regulation. On the other hand, muralists see opportunities to develop their public and professional arts careers and use their art to raise awareness to environmental, cultural, political, and social justice issues. Social relations which have emerged from new configurations of work and art have also produced new subjectivities, perspectives, and worldviews which can help to expand rather than detract from counter-hegemonic struggles. This research will show how graffiti and street artists and the multiplicity of social spaces where they find themselves working have contributed to an expansion of the field of artistic intervention. As such, this research probes the struggles and tensions produced by these new and changing social relations and spatial forms surrounding their professionalization and the popularization through street art muralism to draw out the contradictions between these commercial and emancipatory projects.