Department of Computer Science
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Item An analysis of rock climbing sport regarding performance, sponsorship, and health(2018) Huynh, Huy; Sobek, Elliott; El-Hajj, Mohamad; Atwal, SunnyThe basis of this work was to extract knowledge using data mining techniques over 2 million records from rock climbing competitions all over the world. The first phase of the project involved heavy data cleaning and preprocessing procedures to prepare the data for the mining models. After the first phase was completed, we explored three main questions: which factors can predict the performance of a competitor, which factors will likely lead to a sponsorship of a competitor and is it possible to predict healthiness of a competitor. This research will not only help rock climbers gain significant insights about their performance, it will also help sports sponsors choose the best candidates to assign their brand to.Item Analyzing factors impacting COVID-19 vaccination rates(2023) Cho, Dongseok; Driedger, Mitchell; Han, Sera; Khan, Noman; Elmorsy, Mohammed; El-Hajj, MohamadSince the approval of the COVID-19 vaccine in late 2020, vaccination rates have varied around the globe. Access to a vaccine supply, mandated vaccination policy, and vaccine hesitancy contribute to these rates. This study used COVID-19 vaccination data from Our World in Data and the Multilateral Leaders Task Force on COVID-19 to create two COVID-19 vaccination indices. The first index is the Vaccine Utilization Index (VUI), which measures how effectively each country has utilized its vaccine supply to doubly vaccinate its population. The second index is the Vaccination Acceleration Index (VAI), which evaluates how efficiently each country vaccinated their populations within their first 150 days. Pearson correlations were created between these indices and country indicators obtained from the World Bank. Results of these correlations identify countries with stronger Health indicators such as lower mortality rates, lower age-dependency ratios, and higher rates of immunization to other diseases display higher VUI and VAI scores than countries with lesser values. VAI scores are also positively correlated to Governance and Economic indicators, such as regulatory quality, control of corruption, and GDP per capita. As represented by the VUI, proper utilization of the COVID-19 vaccine supply by country is observed in countries that display excellence in health practices. A country’s motivation to accelerate its vaccination rates within the first 150 days of vaccinating, as represented by the VAI, was largely a product of the governing body’s effectiveness and economic status, as well as overall excellence in health practises.Item Analyzing patterns of car speeding in an urban environment using multivariate functional data clustering(2023) Smith, Iain; Dobosz, Dominic; El-Hajj, MohamadTraffic flow and speed differences between cars are important factors that indicate the likelihood and danger of collisions. A vital part of intelligent transportation systems is discovering important locations to monitor and ticket speeding vehicles. To find these locations, we study data from a low-density city. We identify three critical road groups that indicate risk levels based on car speed differences and weather conditions. We find that these groups have differing weekly trends, which allow traffic enforcement time to change locations to enforce them. We create an analysis that an intelligent transportation system could automate to reduce risk on these roads and save city resources on enforcement.Item Artificial intelligence approaches to build ticket to ride maps(2022) Smith, Iain; Anton, CalinFun, as a game trait, is challenging to evaluate. Previous research explores game arc and game refinement to improve the quality of games. Fun, for some players, is having an even chance to win while executing their strategy. To explore this, we build boards for the game Ticket to Ride while optimizing for a given win rate between four AI agents. These agents execute popular strategies human players use: one-step thinking, long route exploitation, route focus, and destination hungry strategies. We create the underlying graph of a map by connecting several planar bipartite graphs. To build the map, we use a multiple phase design, with each phase implementing several simplified Monte Carlo Tree Search components. Within a phase, the components communicate with each other passively. The experiments show that the proposed approach results in improvements over randomly generated graphs and maps.Item An association analysis of breast cancer with carotenoids(2023) Neumann, Samuel; El-Hajj, MohamadThe environment and the exposure individuals carry throughout their lifetime can gar- ner diverse effects on their health. This paper discusses the application of association analysis, to determine relationships between carcinogenesis and the human exposome. Human exposome data from the World Health Organization was analyzed to determine associations between human exposure and breast cancer. The discovered associations outline specific factors that may be associated with the prevention or causation of breast cancer. We discovered an association between biomarkers in specific biospecimens and breast cancer. Xanthophylls, measured in two different biospecimens, were determined to be associated with American breast cancer patients. The associations discovered may be of use in future cancer studies. This research is particularly interesting because of xanthophylls’ relationship to retinol, inhibiting oncogenesis. Providing support and data for such associations will encourage more research on the exposome’s effect on breast cancer and other conditions.Item Breach path detection reliability in energy harvesting wireless sensor networks(2021) Abougamila, Salwa; Elmorsy, Mohammed; Elmallah, Ehab S.In this paper, we consider reliability assessment of energy harvesting wireless sensor networks (EH-WSNs) deployed to guard a geographic area against intruders that can enter and exit the network through a known set of entry-exit perimeter sides. To handle energy fluctuations during different time slots, a node may reduce its transmission power. Using a probabilistic graph model, we formalize a problem denoted EH-BPDREL (for breach path detection reliability). The problem calls for estimating the likelihood that any such intrusion can be detected and reported to a sink node. Due to the hardness of the problem, bounding algorithms are needed. We devise an efficient algorithm to solve a core problem that facilitates the design of various lower bounding algorithms. We obtain numerical results on the use of Monte Carlo simulation to estimate the probabilistic graph parameters, and illustrate the use of our devised algorithm to bound the solutions.Item Comparisons between text-only and multimedia tweets on user engagement(2020) Indratmo, Indratmo; Zhao, Michael; Buro, KarenHaving highly engaged followers on social media allows us to spread information, seek feedback, and promote a sense of community efficiently. Crafting engaging posts, however, requires careful thoughts, creativity, and communication skills. This research studied tweets and explored the effect of content types on user engagement. More specifically, we compared the number of likes and retweets between text-only and multimedia tweets. We analyzed four Twitter accounts relevant to the City of Edmonton, Canada, and performed negative binomial regressions to model the expected count of likes and retweets based on accounts, content types, and their interaction. The results showed that multimedia content increased engagement in two of the four accounts but did not change engagement significantly in the other two. In other words, multimedia content had a positive or neutral effect on user engagement, depending on accounts. Our analysis also showed the effectiveness of well-written texts in attracting the attention of users. Tweets, by design, are text-oriented, and posting multimedia content may help, but is not a necessary condition to engage with followers effectively on Twitter.Item A deep level set method for image segmentation(2017) Tang, Min; Valipour, Sepehr; Zhang, Zichen; Cobzas, Dana; Jagersand, MartinThis paper proposes a novel image segmentation approach that integrates fully convolutional networks (FCNs) with a level set model. Compared with a FCN, the integrated method can incorporate smoothing and prior information to achieve an accurate segmentation. Furthermore, different than using the level set model as a post-processing tool, we integrate it into the training phase to fine-tune the FCN. This allows the use of unlabeled data during training in a semi-supervised setting. Using two types of medical imaging data (liver CT and left ventricle MRI data), we show that the integrated method achieves good performance even when little training data is available, outperforming the FCN or the level set model alone.Item Developmental hip dysplasia diagnosis at three-dimensional US: a multicenter study(2018) Zonoobi, Dornoosh; Hareendranathan, Abhilash; Mostofi, Emanuel; Mabee, Myles; Pasha, Saba; Cobzas, Dana; Rao, Padma; Dulai, Sukhdeep K.; Kapur, Jeevesh; Jaremko, Jacob L.Purpose: To validate accuracy of diagnosis of developmental dysplasia of the hip (DDH) from geometric properties of acetabular shape extracted from three-dimensional (3D) ultrasonography (US).Item Discriminative analysis of regional evolution of iron and myelin/calcium in deep gray matter of multiple sclerosis and healthy subjects(2018) Elkady, Ahmed M.; Cobzas, Dana; Sun, Hongfu; Blevins, Gregg; Wilman, Alan H.Background: Combined R2* and quantitative susceptibility (QS) has been previously used in cross‐sectional multiple sclerosis (MS) studies to distinguish deep gray matter (DGM) iron accumulation and demyelination. Purpose: We propose and apply discriminative analysis of regional evolution (DARE) to define specific changes in MS and healthy DGM. Study Type: Longitudinal (baseline and 2‐year follow‐up) retrospective study. Subjects: Twenty‐seven relapsing‐remitting MS (RRMS), 17 progressive MS (PMS), and corresponding age‐matched healthy subjects. Field Strength/Sequence: 4.7T 10‐echo gradient‐echo acquisition. Assessment: Automatically segmented caudate nucleus (CN), thalamus (TH), putamen (PU), globus pallidus, red nucleus (RN), substantia nigra, and dentate nucleus were retrospectively analyzed to quantify regional volumes, bulk mean R2*, and bulk mean QS. DARE utilized combined R2* and QS localized changes to compute spatial extent, mean intensity, and total changes of DGM iron and myelin/calcium over 2 years. Statistical Tests: We used mixed factorial analysis for bulk analysis, nonparametric tests for DARE (α = 0.05), and multiple regression analysis using backward elimination of DGM structures (α = 0.05, P = 0.1) to regress bulk and DARE measures with the follow‐up Multiple Sclerosis Severity Score (MSSS). False detection rate correction was applied to all tests. Results: Bulk analysis only detected significant (Q ≤ 0.05) interaction effects in RRMS CN QS (η = 0.45; Q = 0.004) and PU volume (η = 0.38; Q = 0.034). DARE demonstrated significant group differences in all RRMS structures, and in all PMS structures except the RN. The largest RRMS effect size was CN total R2* iron decrease (r = 0.74; Q = 0.00002), and TH mean QS myelin/calcium decrease for PMS (r = 0.70; Q = 0.002). DARE iron increase using total QS demonstrated the highest correlation with MSSS (r = 0.68; Q = 0.0005).Data Conclusion: DARE enabled discriminative assessment of specific DGM changes over 2 years, where iron and myelin/calcium changes were the primary drivers in RRMS and PMS compared to age‐matched controls, respectively. Specific DARE measures of MS DGM correlated with follow‐up MSSS, and may reflect complex disease pathology.Item Discriminative analysis of regional evolution of iron and myelin/calcium in deep gray matter of multiple sclerosis and healthy subjects(2018) Elkady, Ahmed M.; Cobzas, Dana; Sun, Hongfu; Blevins, Gregg; Wilman, Alan H.Combined R2* and quantitative susceptibility (QS) has been previously used in cross‐sectional multiple sclerosis (MS) studies to distinguish deep gray matter (DGM) iron accumulation and demyelination. We propose and apply discriminative analysis of regional evolution (DARE) to define specific changes in MS and healthy DGM. Longitudinal (baseline and 2‐year follow‐up) retrospective study. Twenty‐seven relapsing‐remitting MS (RRMS), 17 progressive MS (PMS), and corresponding age‐matched healthy subjects . Field Strength/Sequence: 4.7T 10‐echo gradient‐echo acquisition. Automatically segmented caudate nucleus (CN), thalamus (TH), putamen (PU), globus pallidus, red nucleus (RN), substantia nigra, and dentate nucleus were retrospectively analyzed to quantify regional volumes, bulk mean R2*, and bulk mean QS. DARE utilized combined R2* and QS localized changes to compute spatial extent, mean intensity, and total changes of DGM iron and myelin/calcium over 2 years. We used mixed factorial analysis for bulk analysis, nonparametric tests for DARE (α = 0.05), and multiple regression analysis using backward elimination of DGM structures (α = 0.05, P = 0.1) to regress bulk and DARE measures with the follow‐up Multiple Sclerosis Severity Score (MSSS). False detection rate correction was applied to all tests. Bulk analysis only detected significant (Q ≤ 0.05) interaction effects in RRMS CN QS (η = 0.45; Q = 0.004) and PU volume (η = 0.38; Q = 0.034). DARE demonstrated significant group differences in all RRMS structures, and in all PMS structures except the RN. The largest RRMS effect size was CN total R2* iron decrease (r = 0.74; Q = 0.00002), and TH mean QS myelin/calcium decrease for PMS (r = 0.70; Q = 0.002). DARE iron increase using total QS demonstrated the highest correlation with MSSS (r = 0.68; Q = 0.0005). DARE enabled discriminative assessment of specific DGM changes over 2 years, where iron and myelin/calcium changes were the primary drivers in RRMS and PMS compared to age‐matched controls, respectively. Specific DARE measures of MS DGM correlated with follow‐up MSSS, and may reflect complex disease pathology.Item The effects of neighbourhood characteristics on crime incidence(2018) Letourneau, Steven; Ell, Nathan; Cheung, Peter; McCaskill, Jordan; El-Hajj, MohamadUsing data from the City of Edmonton, Canada Open Data Portal, an exploration process is undergone using data mining techniques to help detect unseen relationships between tangible spatial characteristics and non-tangible crime incidences. These findings will help law enforcement and city planners make empirically based decisions and avoid the misappropriation of public resources. Using frequent pattern analysis to examine neighbourhood attributes that occur alongside crime provides insight into why crime occurs. These techniques include clustering, classification algorithms, and association algorithms. Results of the analysis on neighbourhood spatial characteristics indicate that dwelling structure type and tree density relate to incidence of neighbourhood crime, while other neighbourhood spatial characteristics bear no relationship. Results also show that intangible neighbourhood characteristics indicate that the distribution of yearly household income and employment and school enrollment levels relate to incidence of neighbourhood crime. The distribution of yearly household income bears a relationship to crime type, specifically violent vs non-violent types.Item The efficacy of stacked bar charts in supporting single-attribute and overall-attribute comparisons(2018) Indratmo, Indratmo; Howorko, Lee; Boedianto, Joyce Maria; Daniel, BenStacked bar charts are a visualization method for presenting multiple attributes of data, and many visualization tools support these charts. To assess the efficacy of stacked bar charts in supporting attribute comparison tasks, we conducted a user study to compare three types of stacked bar charts: classical, inverting, and diverging. Each chart type was used to visualize six attributes of data where half of the attributes have the characteristics of ‘lower better’ whereas the other half ‘higher better.’ Thirty participants were asked to perform two types of comparison tasks: single-attribute and overall-attribute comparisons. We measured the completion time, error rate, and perceived difficulty of the comparison tasks. The results of the study suggest that, for overall-attribute comparisons, the inverting stacked bar chart was the most effective with regards to the completion time. The results also show that performing overall-attribute comparisons using the classical and diverging stacked bar charts required more time than performing single-attribute comparisons using these charts. Participants perceived the inverting and diverging stacked bar charts as easier-to-use than the classical stacked bar chart for overall-attribute comparisons. However, for single-attribute comparisons, all chart types delivered similar performance. We discuss how these findings can inform the better design of interactive stacked bar charts and visualization tools.Item End-to-end detection-segmentation network with ROI convolution(2018) Zhang, Zichen; Tang, Min; Cobzas, Dana; Zonoobi, Dornoosh; Jagersand, Martin; Jaremko, Jacob L.We propose an end-to-end neural network that improves the segmentation accuracy of fully convolutional networks by incorporating a localization unit. This network performs object localization first, which is then used as a cue to guide the training of the segmentation network. We test the proposed method on a segmentation task of small objects on a clinical dataset of ultrasound images. We show that by jointly learning for detection and segmentation, the proposed network is able to improve the segmentation accuracy compared to only learning for segmentation.Item Evaluation of automated computed tomography segmentation to assess body composition and mortality associations in cancer patients(2020) Cespedes Feliciano, Elizabeth M.; Popuri, Karteek; Cobzas, Dana; Baracos, Vickie E.; Beg, Mirza Faisal; Khan, Arafat Dad; Ma, Cydney; Chow, Vincent; Chow, Vincent; Prado, Carla M.; Xiao, Jingjie; Liu, Vincent; Chen, Wendy Y.; Meyerhardt, Jeffrey; Albers, Kathleen B.; Caan, Bette J.Background Body composition from computed tomography (CT) scans is associated with cancer outcomes including surgical complications, chemotoxicity, and survival. Most studies manually segment CT scans, but Automatic Body composition Analyser using Computed tomography image Segmentation (ABACS) software automatically segments muscle and adipose tissues to speed analysis. Here, we externally evaluate ABACS in an independent dataset. Methods Among patients with non‐metastatic colorectal (n = 3102) and breast (n = 2888) cancer diagnosed from 2005 to 2013 at Kaiser Permanente, expert raters annotated tissue areas at the third lumbar vertebra (L3). To compare ABACS segmentation results to manual analysis, we quantified the proportion of pixel‐level image overlap using Jaccard scores and agreement between methods using intra‐class correlation coefficients for continuous tissue areas. We examined performance overall and among subgroups defined by patient and imaging characteristics. To compare the strength of the mortality associations obtained from ABACS's segmentations to manual analysis, we computed Cox proportional hazards ratios (HRs) and 95% confidence intervals (95% CI) by tertile of tissue area. Results Mean ± SD age was 63 ± 11 years for colorectal cancer patients and 56 ± 12 for breast cancer patients. There was strong agreement between manual and automatic segmentations overall and within subgroups of age, sex, body mass index, and cancer stage: average Jaccard scores and intra‐class correlation coefficients exceeded 90% for all tissues. ABACS underestimated muscle and visceral and subcutaneous adipose tissue areas by 1–2% versus manual analysis: mean differences were small at −2.35, −1.97 and −2.38 cm2, respectively. ABACS's performance was lowest for the <2% of patients who were underweight or had anatomic abnormalities. ABACS and manual analysis produced similar associations with mortality; comparing the lowest to highest tertile of skeletal muscle from ABACS versus manual analysis, the HRs were 1.23 (95% CI: 1.00–1.52) versus 1.38 (95% CI: 1.11–1.70) for colorectal cancer patients and 1.30 (95% CI: 1.01–1.66) versus 1.29 (95% CI: 1.00–1.65) for breast cancer patients. Conclusions In the first study to externally evaluate a commercially available software to assess body composition, automated segmentation of muscle and adipose tissues using ABACS was similar to manual analysis and associated with mortality after non‐metastatic cancer. Automated methods will accelerate body composition research and, eventually, facilitate integration of body composition measures into clinical care.Item Five year iron changes in relapsing-remitting multiple sclerosis deep gray matter compared to healthy controls(2019) Elkady, Ahmed M.; Cobzas, Dana; Sun, Hongfu; Seres, Peter; Blevins, Gregg; Wilman, Alan H.Relapsing-Remitting MS (RRMS) Deep Grey Matter (DGM) 5 year changes were examined using MRI measures of volume, transverse relaxation rate (R2*) and quantitative magnetic susceptibility (QS). By applying Discriminative Analysis of Regional Evolution (DARE), R2* and QS changes from iron and non-iron sources were separated. 25 RRMS and 25 age-matched control subjects were studied at baseline and 5-year follow-up. Bulk DGM mean R2* and QS of the caudate nucleus, putamen, thalamus and globus pallidus were analyzed using mixed factorial analysis (α = 0.05) with sex as a covariate, while DARE employed non-parametric analysis to study regional changes. Regression/correlation analysis was performed with disease duration and MS Severity Score (MSSS). No significant change in Extended Disability Status Score was found over 5 years (baseline = 2.4 ± 1.2; follow-up = 2.8 ± 1.3). Significant time effects were found for R2* in the caudate (Q = 0.000008; η2 = 0.36), putamen (Q = 0.0000007; η2 = 0.43), and globus pallidus (Q = 0.0000007; η2 = 0.43), while significant longitudinal effects were only found for QS in the putamen (Q = 0.002; η2 = 0.22). Significant bulk interaction was only found for thalamus volume (Q = 0.02; η2 = 0.20). Iron decrease was the only detected significant effect using DARE, and the highest significant DARE effect size was mean thalamus R2* iron decrease (Q = 0.002; η2 = 0.26). No significant correlations or regressions were demonstrated with clinical measures. Thalamic atrophy was the only bulk effect that demonstrated different rates of changes over 5 years compared to age-matched controls. DARE Iron decrease in regions of the caudate, putamen, and thalamus were prominent features in stable RRMS over 5 years.Item GUI tools made easy: interact with models and explore data(2015) Schnute, Jon; Couture-Beil, Alex; Haugh, Rowan; Kronlund, Rob; Boers, NicholasProvides software to facilitate the design, testing, and operation of computer models. It focuses particularly on tools that make it easy to construct and edit a customized graphical user interface (GUI). Although our simplified GUI language depends heavily on the R interface to the Tcl/Tk package, a user does not need to know Tcl/Tk. Examples illustrate models built with other R packages, including PBSmapping, PBSddesolve, and BRugs. A complete user's guide `PBSmodelling-UG.pdf' shows how to use this package effectively.Item Hippocampus segmentation on high resolution dffusion MRI(2021) Efird, Cory; Neumann, Samuel; Solar, Kevin G.; Beaulieu, Christian; Cobzas, DanaWe introduce the first hippocampus segmentation method for a novel high resolution (1×1×1mm3) diffusion tensor imaging (DTI) protocol acquired in 5.5 minutes at 3T. A new augmentation technique uses subsets of the DTI dataset to create mean diffusion weighted images (DWI) with plausible noise and contrast variations. The augmented DWI along with fractional anisotropy (FA) and mean diffusivity (MD) maps are used as inputs to a powerful convolutional neural network architecture. The method is evaluated for robustness using a second diffusion protocol.Item Hy-bridge: a hybrid blockchain for privacy-preserving and trustful energy transactions in Internet-of-Things platforms(2020) Firoozjaei, Mahdi Daghmehchi; Ghorbani, Ali; Kim, Hyoungshick; Song, JaeSeungIn the current centralized IoT ecosystems, all financial transactions are routed through IoT platform providers. The security and privacy issues are inevitable with an untrusted or compromised IoT platform provider. To address these issues, we propose Hy-Bridge, a hybrid blockchain-based billing and charging framework. In Hy-Bridge, the IoT platform provider plays no proxy role, and IoT users can securely and efficiently share a credit with other users. The trustful end-to-end functionality of blockchain helps us to provide accountability and reliability features in IoT transactions. Furthermore, with the blockchain-distributed consensus, we provide a credit-sharing feature for IoT users in the energy and utility market. To provide this feature, we introduce a local block framework for service management in the credit-sharing group. To preserve the IoT users’ privacy and avoid any information leakage to the main blockchain, an interconnection position, called bridge, is introduced to isolate IoT users’ peer-to-peer transactions and link the main blockchain to its subnetwork blockchain(s) in a hybrid model. To this end, a k-anonymity protection is performed on the bridge. To evaluate the performance of the introduced hybrid blockchain-based billing and charging, we simulated the energy use case scenario using Hy-Bridge. Our simulation results show that Hy-Bridge could protect user privacy with an acceptable level of information loss and CPU and memory usage.Item Internationalizing the student experience through computing for social good(2020) Aheer, Komal; Bauer, Ken; Macdonell, CamInformation technology has connected our world and its citizens in incredible ways. Despite this connectedness, students are often isolated within the "online bubbles" of their own university, city, or country. Technology provides a great opportunity to connect them to a broader global experience. We have developed and piloted a cross-institution activity as part of an Internationalization at Home (IaH) initiative to expose first year computer science students to the concept of computing for social good in an international context. We explore how differences in culture can influence students' perceptions and approaches to computing for social good. Specifically, we had students from a Mexican and a Canadian university explore how computing for social good could be used to solve issues they faced in their communities. Students participated in surveys to propose and then rank applications for social good. The students also participated in a videoconference discussion with the students from the other school to discuss their choices. Thematic analysis revealed that the students had much more in common with each other than they had differences. Both groups not only focused on similar areas of interest, but they also tended to focus on solving issues with a local scope rather than national or global scope. Despite their cultural differences, the majority students felt they were more similar to their peers of the other culture than they were different.
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