Dynamic Calculation of Password Salts for Improved Resilience towards Password Cracking Algorithms

Accepted version of manuscriptPasswords have been an integral part of our lives from the dawn of the internet and keeping them secure has been of paramount importance. Each attempt to secure our digital lives has been met with increased complexity and scope in attacks to compromise security measures. This paper explores a novel methodology to calculate password salts by using the password itself and multiple texts to generate lookup values into a text corpus that is then used to calculate salt values dynamically and on the fly. The proposed method allows an authentication system to use salts for password storage without storing the salts in a database where they might be compromised.https://ieeexplore.ieee.org/document/1052731

Wheat Head Classification in 3D Point Clouds for Fusarium Head blight Detection

Deep learning (DL) has become one of the most efficient tools for data processing in computer vision and is a popular technique for tasks such as classification, segmentation, and detection. Although most of these techniques have been applied to data with a structured grid, 3D data such as point clouds have shown proficient results and increased popularity due to the growing availability of acquisition devices. This has led to their application in areas such as robotics, autonomous driving, medicine, agriculture, and more. A point cloud is a set of points defined in a 3D metric space, characterized by its unstructured nature. The unstructuredness of point clouds makes the use of DL for direct processing challenging and 3D object detection has become an active research topic. 3D object detection is an important functional method as it can simultaneously predict surrounding objects' categories, locations, and sizes. In fields like agriculture, this technique offers the potential to analyse various plant attributes, such as plant height, biomass, and the number and size of relevant plant organs.Plant detection and recognition represent a difficult challenge due to the plants' size, posture, shape, illumination, and texture, which vary depending on the varieties and growth stages. One major challenge is presented in wheat plants. As a fundamental source of food, the interest in its analysis has increased. Detection of wheat spikes can help validate spikelet fertility, spike characteristics, and evaluate high-yield wheat cultivars. In this thesis, we created a dataset of 576 point cloud data samples of multiple wheat plants, which we manually labeled for computer vision tasks such as object detection and wheat head classification. Utilizing a 3D neural network model specialized for point clouds, called PointNet, we developed a 3D object detection model to identify and detect wheat heads. This model allowed us to use point clouds directly as input data to preserve the detailed point information. The results demonstrated a test accuracy of 80% in the best model. Finally, a 3D CNN-based classification model was integrated to develop a wheat head classification model for 3D point clouds for Fusarium Head blight (FHB) detection. The classification model was fine-tuned for disease detection to automatically identify wheat infected with FHB from 3D images of wheat heads. The model for FHB detection in wheat spikelets achieved 91% accuracy in a multiple wheat plants test set. Extensive cross-validation experiments were performed to evaluate the performance ability of the model with promising detection results. In addition, the drawbacks of the proposed method were analyzed, and directions for future work are provided.Master of Science in Applied Computer Scienc

Safely navigating the "dangerous space between good intentions and meaningful interventions": A study on the use of school suspensions in Manitoba Canada

This qualitative study delves into the perspectives of school leaders in a Canadian province, exploring their views on student suspensions and alternative approaches to school discipline. Amid a provincial advocacy organization’s call for a review and reduction of suspensions in that jurisdiction, the study captures both constructive and critical views of school leaders on the practice. Findings reveal a general endorsement of the authority to suspend students under specific conditions. School leaders are, however, conflicted about what constitutes the condition of imminent safety risk, and they advocate for discretion in making that determination. Proposing progressive-discipline strategies as alternatives, they underscore the necessity of available external resources from provincial social systems to ensure the viability and success of suggested alternatives. Collectively, this study navigates the landscape of school discipline, emphasizing the delicate balance between maintaining order and fostering an environment that is conducive for all involved in supporting student success.https://ojs.lib.uwo.ca/index.php/eei/article/view/1715

The role of ambient temperature in the ontogeny of endangered Oarisma poweshiek and their relative O. garita reared ex-situ at Assiniboine Park Zoo, Manitoba

Regionwide extirpations of Poweshiek skipperlings (Oarisma poweshiek) have prompted an international conservation effort to understand the causes of their decline and to recover the species. However, aspects of their basic biology remain unknown: in particular the role of temperature in their development and their sensitivity to climate change. I studied degree day (DD) accumulations in Poweshiek skipperling and its sister species, Garita skipperling (O. garita), from egg hatch to eclosure. I calculated the DDs accrued by both species reared from 2017 to 2020 at Assiniboine Park Zoo, Manitoba, and compared them between generations and species. I also compared the variability in their hatch dates, the start and end of overwintering, pupation dates, and eclosure dates for each generation and their ages (in days) at each stage. I used thermal upper and lower development thresholds of 32 ℃ and 6 ℃ using the standard and double-sine method to calculate degree-days as well as using the double-sine method without an upper threshold. I calculated similar DDs for both species, as well as similar averages within generations, hatch and eclosure dates and final ages at eclosure. However, Garita skipperlings exhibited substantially more DD accumulation variation, within and between generations, except for the pupal duration where Poweshiek skipperlings were more variable. Poweshiek and Garita skipperling DDs were more variable between generations than within generations, suggesting that variables other than temperature (such as photoperiod) may influence the synchronization of adult emergence. While Poweshiek and Garita skipperlings had similar eclosure dates, final ages, and DD accumulation across the generations, the larger variation observed in most developmental stages in Garita skipperling could suggest that they are more resilient to the effects of climate change. I also reared Garita skipperling larvae in constant, elevated temperatures and compared their growth and survivorship to larvae reared in natural, diurnal temperatures. I measured head capsules widths with an ocular micrometer and determined the total number of instars for Garita skipperling that survived to eclosure. Larvae were grouped using Dyar’s Values, without assuming Dyar’s Rule, then analyzed with k-means clustering to estimate the instar at each measurement. Garita skipperlings eclosed after five, six, or seven instars, and did not maintain a consistent size ratio between instars as assumed by Dyar’s Rule. While larvae with five, six, or seven instars were observed surviving to eclosure in both diurnal and static temperature regimes, the affect of treatment on larval instar number was indeterminable. These results cast doubt on the generalizability of Dyar’s Rule in instar determination for these species and reinforce previous authors’ conclusions that using statistical analysis without applying Dyar’s Rule may be more accurate. Experimental temperature regimes were used to investigate the affect of elevated temperatures on growth of Garita skipperling. Larvae that were reared in these trials were placed into environmental chambers held at 28 ℃, 21 ℃, or reared outdoors in the control. Two groups of larvae were added to the 28 ℃ environmental chamber: neonates added by the beginning of August, and larvae added at the end of August (late inductees, hereafter), after several weeks of development. Only neonates were placed into the 21 ℃ chamber and into the control group. I compared the survival, final ages (in days), and DD accumulation of the larvae that survived to eclosure in each treatment. The 21 ℃-trial had 15% lower and the 28 ℃ trials 6% higher (or 57% higher for late inductees) survivorship versus larvae reared outdoors. However, neonate mortality could not be calculated for late inductees to the 28 ℃-trial. Temperature appeared to influence the differences between my 28 ℃ trial and control. The results of the 21 ℃ treatment are difficult to interpret as potential equipment failure likely reduced the survival of this treatment group. The phenology of almost all larvae in the 28 ℃ chamber was extremely accelerated and almost all survivors eclosed in the year they hatched. All other larvae that survived to adulthood, regardless of treatment, eclosed at the same time as the control group. Larvae that eclosed in a single season and larvae in the control had comparable DDs. Larvae in both treatments that eclosed after overwintering had substantially higher DDs at eclosure than the control group. Photoperiod may provide cues to prepare for hibernation, and/or when to eclose, thus may be responsible for the similar eclosure dates of larvae that overwintered in all treatments. However, its role was not estimable because photoperiod was matched in the treatments and control. Poweshiek and Garita skipperlings currently have similar ontogenies in Manitoba, but the greater variability in the development of Garita skipperling seen in this study, and their longer wild flight period, suggests an increased resilience to climate change. My temperature manipulation experiments provide evidence that high static temperatures will accelerate most larvae exposed within several weeks of hatching, causing them to eclose before overwintering, and suggests that rapid climate change could be a factor in loss of Poweshiek skipperling populations as they have lower variability in their developmental rates. My results also suggest that overwintering may act to “reset” development so that individuals emerge in synchrony with conspecifics, also suggesting added resilience in Garita skipperling. Further research on the precise DD accumulation thresholds throughout development for both species is needed to determine the risk and severity of climate change induced phenological shifts in Poweshiek and Garita skipperlings.MITACS; Assiniboine Park ZooMaster of Science in Bioscience, Technology, and Public Polic

Advancing EEG-Based Emotion Recognition: Multimodal Techniques, Channel Optimization, and Insights into Subjective Emotion Perception

This dissertation explores the application of electroencephalography (EEG) in identifying and understanding the neural mechanisms of emotional responses. Using a non-invasive and economically efficient OpenBCI Cyton wireless EEG system, this research developed and assessed several technological advancements for optimal neural activity recording. Key among these were the implementation of both software and hardware triggers to ensure precise data acquisition and a comparative analysis of dry versus semi-dry electrodes. The findings suggest that semi-dry electrodes, when used in conjunction with a flexible cap, not only minimize noise but also improve participant comfort, thereby offering clear benefits over the dry electrodes with rigid cap structure by OpenBCI. Additionally, our study of event-related potentials (ERP), which included measures of subjective emotional responses, indicates that the impact of motion versus no motion in stimuli diminishes at higher emotional intensities. This observation suggests that at elevated levels of emotional arousal, the emotional content of the stimuli becomes more salient to the perceiver than the associated motoric information. Moreover, this thesis delineates the development of a deep learning model dedicated to emotion recognition, which successfully achieved a prediction accuracy of 72.3% by incorporating EEG and eye movement data. However, the practical challenges associated with multimodal data collection, particularly among older adults and individuals with neurological disorders, are pronounced. The use of 62 EEG channels, for instance, can be cumbersome and uncomfortable. This challenge has spurred further investigation into the transferability and generalizability of EEG channel selection tailored for emotion recognition tasks across different datasets. Employing a dataset-independent strategy and leveraging Power Spectral Density (PSD) to pinpoint critical EEG channels, our methodology was validated across independent dataset using a Convolutional Neural Network (CNN). Through comprehensive experiments that varied the number of channels and features, our models exhibited classification accuracies of 77.02%, 75.42%, 71.31%, and 64.31% with configurations of 62, 30, 20, and 10 EEG channels, respectively, for four distinct emotion categories. This approach to channel selection not only streamlined the number of EEG channels necessary for accurate emotion prediction but also paved the way for the development of more efficient EEG systems. Such systems are envisioned to facilitate daily emotional monitoring in individuals with neurodegenerative diseases. Lastly, the research also introduced a deep learning model capable of predicting subjective emotional intensities, which achieved an F1-Score of 65.2% in classifying between high and low emotional intensities. This result underscores the significant impact of the subjectivity of emotions on the efficacy of emotion recognition technologies.University of Winnipeg, Misericordia Health Center, Mitacs, and NSERC.Master of Applied Computer Scienc

UWinnipeg Feedback for Tri-Agency Open Access Policy Review

Submission for the 2024 Tri-Agency Open Access Policy on Publications Review. https://science.gc.ca/site/science/en/interagency-research-funding/policies-and-guidelines/open-access/presidents-canadas-federal-research-granting-agencies-announce-review-tri-agency-open-access-polic

Structure, Function and Drought Resilience of Northern Prairie Communities, 50 Years After Grazing Disturbance

With climate change threatening the function of grassland ecosystems, conservation and restoration strategies are shifting from comparisons of species compositions with baseline conditions, to assessments of ecosystem functions and resilience. Here, I present research from Riding Mountain National Park, Manitoba, to illustrate the links between plant community composition, leaf traits of dominant plants, and grassland community function. I also discuss applications for the management of grassland ecosystems. I use plant community data, collected in 1973, 2010 and 2020, to understand the long-term effects of grazing on the function and resilience of northern fescue prairies. I test whether legacies of historic grazing continue to affect the structure, diversity, and composition of grassland communities, and whether historic grazing predicted community leaf trait composition in fescue grassland ecosystems. I also explore how nutrient and carbon cycling may be influenced by leaf traits of dominant plants, including their leaf carbon and nitrogen concentrations. Fifty years after grazing, heavily grazed prairies continued to have lower plant diversity. However, prairies with light grazing had lower spatial variation in plant composition. By 2020, community leaf trait composition could not be predicted by historic grazing, and instead, plant trait composition was driven by exotic species invasions. Similarity in traits between Poa pratensis and Festuca hallii resulted in a functional redundancy between lightly and heavily grazed grasslands. Invasions of Poa pratensis increased the values of leaf density, and leaf C:N over the years while Bromus inermis increased the value of specific leaf area (SLA), illustrating that changes in grassland composition correlate with changes in the traits of dominant plants that have the potential to affect the resilience of grasslands to drought as well as their function. This study describes how community trait composition can impact grassland drought tolerance and ecosystem functions, and the management implications of those consequences.Master of Science in Bioscience, Technology, and Public Polic

Source and fate of dissolved organic matter in boreal headwater streams

Understanding the source and fate of dissolved organic matter (DOM), a key water quality variable, in boreal headwaters is of critical importance considering the amount of carbon stored and processed in different ecosystem components within the boreal forest and the sensitivity of these processes to climate change. Using historical streamflow and stream chemistry data in combination with direct measurements of the landscape sources of DOM and more detailed stream DOM quality data from 2021 at the IISD-ELA, I examined how the terrestrial source of DOM influences the quantity and quality of DOM in three boreal headwater streams. Using historical stream data from 1981-2021, I found that concentration-discharge (c-Q) relationships varied based on both catchment characteristics and hydrological conditions. Streams draining upland-dominated catchments were more often transport-limited (i.e., concentration increased with increasing flow), whereas a wetland-dominated stream was more often source-limited (i.e., concentration decreased with increasing flow) in terms of stream DOM concentration. DOM concentration and quality data in soil leachate indicated that streamwater had DOM characteristics suggesting it originated from near-stream organic soils, while after the drought the DOM came proportionally more from distal mineral soils (in addition to near-stream organic soil contributions). I showed that the severe drought in 2021 made streams with varying landscape characteristics respond similarly to the post-drought flush. These findings also illustrate that while c-Q relationships may be different among streams draining upland-dominated and wetland-dominated catchments as a result of the different abilities of these landscape to accumulate and mobilize DOM, DOM quality responded to this drought to post-drought flush synchronously among all three streams. As climate change will alter the frequency, duration, and severity of future hydrological conditions, this has repercussions for the DOM dynamics in headwater streams and the resulting water quality downstream."I was supported in this research by a UWGSS Scholarship from UWinnipeg and an NSERc - Canada Graduate Scholarship."Master of Science in Bioscience, Technology and Public Polic

Exploring Hyperspectral Imaging and 3D Convolutional Neural Network for Stress Classification in Plants

Hyperspectral imaging (HSI) has emerged as a transformative technology in imaging, characterized by its ability to capture a wide spectrum of light, including wavelengths beyond the visible range. This approach significantly differs from traditional imaging methods such as RGB imaging, which uses three color channels, and multispectral imaging, which captures several discrete spectral bands. Through this approach, HSI offers detailed spectral signatures for each pixel, facilitating a more nuanced analysis of the imaged subjects. This capability is particularly beneficial in applications like agricultural practices, where it can detect changes in physiological and structural characteristics of crops. Moreover, the ability of HSI to monitor these changes over time is advantageous for observing how subjects respond to different environmental conditions or treatments. However, the high-dimensional nature of hyperspectral data presents challenges in data processing and feature extraction. Traditional machine learning algorithms often struggle to handle such complexity. This is where 3D Convolutional Neural Networks (CNNs) become valuable. Unlike 1D-CNNs, which extract features from spectral dimensions, and 2D-CNNs, which focus on spatial dimensions, 3D CNNs have the capability to process data across both spectral and spatial dimensions. This makes them adept at extracting complex features from hyperspectral data. In this thesis, we explored the potency of HSI combined with 3D-CNN in agriculture domain where plant health and vitality are paramount. To evaluate this, we subjected lettuce plants to varying stress levels to assess the performance of this method in classifying the stressed lettuce at the early stages of growth into their respective stress-level groups. For this study, we created a dataset comprising 88 hyperspectral image samples of stressed lettuce. Utilizing Bayesian optimization, we developed 350 distinct 3D-CNN models to assess the method. The top-performing model achieved a 75.00\% test accuracy. Additionally, we addressed the challenge of generating valid 3D-CNN models in the Keras Tuner library through meticulous hyperparameter configuration. Our investigation also extends to the role of individual channels and channel groups within the color and near-infrared spectrum in predicting results for each stress-level group. We observed that the red and green spectra have a higher influence on the prediction results. Furthermore, we conducted a comprehensive review of 3D-CNN-based classification techniques for diseased and defective crops using non-UAV-based hyperspectral images.MITACSMaster of Science in Applied Computer Scienc

Mobility of arsenic and vanadium in waterlogged calcareous soils due to addition of zeolite and manganese oxide amendments

Addition of manganese(IV) oxides (MnO2) and zeolite can affect the mobility of As and V in soils due to geochemical changes that have not been studied well in calcareous, flooded soils. This study evaluated the mobility of As and V in flooded soils surface-amended with MnO2 or zeolite. A simulated summer flooding study was conducted for 8 weeks using intact soil columns from four calcareous soils. Redox potential was measured in soils, whereas pH, major cations, and As and V concentrations were measured biweekly in pore water and floodwater. Aqueous As and V species were modeled at 0, 4, and 8 weeks after flooding (WAF) using Visual MINTEQ modeling software with input parameters of redox potential, temperature, pH, total alkalinity, and concentrations of major cations and anions. Aqueous As concentrations were below the critical thresholds (<100 μg L−1), whereas aqueous V concentrations exceeded the threshold for sensitive aquatic species (2–80 μg L−1). MnO2-amended soils were reduced to sub-oxic levels, whereas zeolite-amended and unamended soils were reduced to anoxic levels by 8 WAF. MnO2 decreased As and V mobilities, whereas zeolite had no effect on As but increased V mobility, compared to unamended soils. Arsenic mobility increased under anoxic conditions, and V mobility increased under oxic and alkaline pH conditions. Conversion of As(V) to As(III) and V(V) to V(IV) was regulated by MnO2 in flooded soils. MnO2 can be used as an amendment in immobilizing As and V, whereas the use of zeolite in flooded calcareous soils should be done cautiously."This research was financially supported by Environment and Climate Change Canada through Lake Winnipeg Basin Program, University of Winnipeg Major Grant and Canadian Queen Elizabeth II Diamond Jubilee Scholarships: Advanced Scholars program."https://acsess.onlinelibrary.wiley.com/doi/10.1002/jeq2.2045