Department of Computer Science

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https://hdl.handle.net/20.500.14078/7

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Research recast(ed): S1E12 - The intersections of computer and medical science with Dr. Dana Cobzas
(2022) Ekelund, Brittany; Cave, Dylan; Cobzas, Dana
Today we enter the world of medical imaging and computer vision, exploring the spaces in which computer science and medical science intersect. Here to help us understand it all is Dr. Dana Cobzas. She is an associate professor in the Department of Computer Science at MacEwan University, and her areas of expertise include imaging and computer vision, with a particular interest in mathematical models for medical imaging processing. You can follow up with Dana’s MS lesion segmentation challenge here: https://portal.fli-iam.irisa.fr/msseg-2/.
Understanding cybersecurity on smartphones : challenges, strategies, and trends
(2024) Kadir, Andi Fitriah Abdul; Lashkari, Arash Habibi; Firoozjaei, Mahdi Daghmehchi
This book offers a comprehensive overview of smartphone security, focusing on various operating systems and their associated challenges. It covers the smartphone industry's evolution, emphasizing security and privacy concerns. It explores Android, iOS, and Windows OS security vulnerabilities and mitigation measures. Additionally, it discusses alternative OSs like Symbian, Tizen, Sailfish, Ubuntu Touch, KaiOS, Sirin, and HarmonyOS. The book also addresses mobile application security, best practices for users and developers, Mobile Device Management (MDM) in enterprise settings, mobile network security, and the significance of mobile cloud security and emerging technologies such as IoT, AI, ML, and blockchain. It discusses the importance of balancing innovation with solid security practices in the ever-evolving mobile technology landscape.
Analyzing patterns of car speeding in an urban environment using multivariate functional data clustering
(2023) Smith, Iain; Dobosz, Dominic; El-Hajj, Mohamad
Traffic 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.
An association analysis of breast cancer with carotenoids
(2023) Neumann, Samuel; El-Hajj, Mohamad
The 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.
Analyzing factors impacting COVID-19 vaccination rates
(2023) Cho, Dongseok; Driedger, Mitchell; Han, Sera; Khan, Noman; Elmorsy, Mohammed; El-Hajj, Mohamad
Since 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.
A UNet pipeline for segmentation of new MS lesions
(2021) Efird, Cory; Miller, Dylan; Cobzas, Dana
A pipeline for the second multiple sclerosis segmentation challenge (MSSEG-2) hosted by MICCAI is proposed. Two FLAIR images taken at different time-points are used as a multi-channel input to a 3D CNN to detect new lesions. Patch sampling strategies are adopted to keep the input volume shape manageable in terms of memory requirements. To further improve results, multiple models and patch orientations are ensembled. Performance is evaluated against nn-UNet.
Stainless steel electrochemical capacitive microneedle sensors for multiplexed simultaneous measurement of pH, nitrates, and phosphates
(2022) Mugo, Samuel; Lu, Weihao; Lemieux, Stephane
Concerns for agri-food safety and environmental management require development of simple to use and cost- and time effective multiplex sensors for point-of-need (PON) chemical analytics by public end-user. Simultaneous detection of nitrates, phosphates, and pH is of importance in soil and water analysis, agriculture, and food quality assessment. This article demonstrates a suite of stainless steel microneedle electrochemical sensors for multiplexed measurement of pH, nitrate, and phosphate using faradaic capacitance derived from cyclic voltammetry as the mode of detection. The multi-target microneedle sensors were fabricated by layer-by-layer (LbL) assembly in a stainless steel hypodermic microneedle substrate. For nitrate sensing, the stainless steel was coated with carbon nanotube/cellulose nanocrystal (CNT)/CNC) decorated with silver nanoparticles (Ag). For pH measurement, the polyaniline (pANI) was coated onto the CNT/CNC@Ag film, while for phosphate detection, the CNT/CNC/Ag @pANI microneedle was further decorated with ammonium molybdenum tetrahydrate (AMT). The microelectrode platforms were characterized by FTIR, Raman, and microscopic techniques. The nitrate- and phosphate-based microneedle electrochemical sensors had excellent selectivity and sensitivity, with a determined limit of detection (LOD) of 0.008 mM and 0.007 mM, respectively. The pH microneedle sensor was responsive to pH in the linear range of 3–10. The three microneedle sensors yielded repeatable results, with a precision ranging from 4.0 to 7.5% RSD over the concentration ranges tested. The inexpensive (~ 1 $ CAD) microneedle sensors were successfully verified for use in quantification of nitrate, pH, and phosphate in brewed black coffee as a real sample. As such, the microneedle sensors are economical devices and show great promise as robust platforms for PON precision chemical analytics.
User testing for serious game design: improving the player experience
(2022) Shaw, Ross; Sperano, Isabelle; Andruchow, Robert; Cobzas, Dana
This case study reflects on our use of user testing during a research project in which we designed a serious video game, “Life on the Edge.” The target audience of the game is first-year post-secondary biology students. As we designed the game, user testing was a critical component that allowed us to identify issues. Any issues that interfere with the flow or enjoyment of a video game can be distracting to players. In what follows, we will describe the research design and discuss the processes for testing a serious video game that will allow you to identify game issues successfully. How you recruit participants, test players, and prioritize player feedback is a component of effective user testing and improving your game. With user testing, we were able to identify problems in the game, prioritize them, and address them. By using variable user testing methods, you can adapt to the changing needs of your game project and develop a successful serious video game.
Novikov groups are right-orderable
(2022) Lemieux, Stephane
Novikov groups were introduced as examples of finitely presented groups with unsolvable conjugacy problem. It was Bokut who showed that each Novikov group has a standard basis and thus a solvable word problem. Further, he showed that for every recursively enumerable degree of unsolvability d there is a Novikov group whose conjugacy problem is of degree d. In the present work, we show that Novikov groups are also right-orderable, thus exhibiting the first known examples of finitely presented right-orderable groups with solvable word problem and unsolvable conjugacy problem.
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.
Artificial intelligence approaches to build ticket to ride maps
(2022) Smith, Iain; Anton, Calin
Fun, 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.
Low-rank plus sparse decomposition of fMRI data with application to Alzheimer's disease
(2022) Tu, Wei; Fu, Fangfang; Kong, Linglong; Jiang, Bei; Cobzas, Dana; Huang, Chao
Studying functional brain connectivity plays an important role in understanding how human brain functions and neuropsychological diseases such as autism, attention-deficit hyperactivity disorder, and Alzheimer's disease (AD). Functional magnetic resonance imaging (fMRI) is one of the most popularly used tool to construct functional brain connectivity. However, the presence of noises and outliers in fMRI blood oxygen level dependent (BOLD) signals might lead to unreliable and unstable results in the construction of connectivity matrix. In this paper, we propose a pipeline that enables us to estimate robust and stable connectivity matrix, which increases the detectability of group differences. In particular, a low-rank plus sparse (L + S) matrix decomposition technique is adopted to decompose the original signals, where the low-rank matrix L recovers the essential common features from regions of interest, and the sparse matrix S catches the sparse individual variability and potential outliers. On the basis of decomposed signals, we construct connectivity matrix using the proposed novel concentration inequality-based sparse estimator. In order to facilitate the comparisons, we also consider correlation, partial correlation, and graphical Lasso-based methods. Hypothesis testing is then conducted to detect group differences. The proposed pipeline is applied to rs-fMRI data in Alzheimer's disease neuroimaging initiative to detect AD-related biomarkers, and we show that the proposed pipeline provides accurate yet more stable results than using the original BOLD signals.
Internationalizing the student experience through computing for social good
(2020) Aheer, Komal; Bauer, Ken; Macdonell, Cam
Information 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.
Comparisons between text-only and multimedia tweets on user engagement
(2020) Indratmo, Indratmo; Zhao, Michael; Buro, Karen
Having 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.
On flow reliability in energy harvesting wireless sensor networks
(2021) Elmorsy, Mohammed; Elmallah, Ehab S.
A basic wireless sensor networks (WSNs) reliability problem calls for finding the likelihood that a sink node receives at least a certain amount of traffic generated periodically by sensor nodes that can either operate or fail. When the nodes rely on harvesting energy from the ambient environment, a node can be in any one of a possible number of energy states with probabilities that can be estimated using measured environmental data. A node’s energy management unit can work by controlling the amount of data that can be periodically transmitted in each state. In this context, we formalize a flow reliability problem (denoted FLOWREL) in EH-WSNs. We present a method for computing lower bounds on exact solutions using an iterative algorithmic framework. Numerical results are presented to examine the performance of the devised methodology. Further, we discuss its use in a sample application that asks for determining the best sink location among a set of candidate locations.
Hippocampus segmentation on high resolution dffusion MRI
(2021) Efird, Cory; Neumann, Samuel; Solar, Kevin G.; Beaulieu, Christian; Cobzas, Dana
We 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.
Wearable microneedle dual electrochemical sensor forsimultaneous pH and cortisol detection in sweat
(2021) Mugo, Samuel; Lu, Weihao; Wood, Marika; Lemieux, Stephane
We report herewith an inexpensive flexible dual target electrochemical sensor for simultaneous detection of pH and cortisol in human sweat. The sensor was fabricated by printing layer by layer (LbL) on a conductive microneedle polydimethylsiloxane (PDMS) flexible substrate. The dual sensor integrates two detection chambers comprising polyaniline (PANi) and cortisol imprinted poly (glycidylmethacrylate-co ethylene glycol dimethacrylate) (poly (GMA-co-EGDMA)). The dual wearable sensor rapidly (< 1 min) responded linearly to pH in the range of 3–9, while the cortisol sensor chamber had a linear range of 0–100 ng/mL. The cortisol sensing region had an excellent limit of detection (LOD) of 1.4 ± 0.3 ng/mL, with intra-batch reproducibility of 2.4% relative standard deviation (%RSD). The inter-batch precision (%RSD for three different sensors) was determined to be 4.7%. Demonstrating excellent stability and reusability, a single patch of cortisol sensor was used for 15 times over a 30-day period, with minimal change in response. The dual analyte wearable sensors were effective for detection of pH and cortisol in real human sweat.
Stable anatomy detection in multimodal imaging through sparse group regularization: a comparative study of iron accumulation in the aging brain
(2021) Pietrosanu, Matthew; Zhang, Li; Seres, Peter; Elkady, Ahmed M.; Wilman, Alan H.; Kong, Linglong; Cobzas, Dana
Multimodal neuroimaging provides a rich source of data for identifying brain regions associated with disease progression and aging. However, present studies still typically analyze modalities separately or aggregate voxel-wise measurements and analyses to the structural level, thus reducing statistical power. As a central example, previous works have used two quantitative MRI parameters—R2* and quantitative susceptibility (QS)—to study changes in iron associated with aging in healthy and multiple sclerosis subjects, but failed to simultaneously account for both. In this article, we propose a unified framework that combines information from multiple imaging modalities and regularizes estimates for increased interpretability, generalizability, and stability. Our work focuses on joint region detection problems where overlap between effect supports across modalities is encouraged but not strictly enforced. To achieve this, we combine L1 (lasso), total variation (TV), and L2 group lasso penalties. While the TV penalty encourages geometric regularization by controlling estimate variability and support boundary geometry, the group lasso penalty accounts for similarities in the support between imaging modalities. We address the computational difficulty in this regularization scheme with an alternating direction method of multipliers (ADMM) optimizer. In a neuroimaging application, we compare our method against independent sparse and joint sparse models using a dataset of R2* and QS maps derived from MRI scans of 113 healthy controls: our method produces clinically-interpretable regions where specific iron changes are associated with healthy aging. Together with results across multiple simulation studies, we conclude that our approach identifies regions that are more strongly associated with the variable of interest (e.g., age), more accurate, and more stable with respect to training data variability. This work makes progress toward a stable and interpretable multimodal imaging analysis framework for studying disease-related changes in brain structure and can be extended for classification and disease prediction tasks.
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.
Systematic review of the literature on big data in the transportation domain: concepts and applications
(2019) Neilson, Alex; Indratmo, Indratmo; Daniel, Ben; Tjandra, Stevanus
Research in Big Data and analytics offers tremendous opportunities to utilize evidence in making decisions in many application domains. To what extent can the paradigms of Big Data and analytics be used in the domain of transport? This article reports on an outcome of a systematic review of published articles in the last five years that discuss Big Data concepts and applications in the transportation domain. The goal is to explore and understand the current research, opportunities, and challenges relating to the utilization of Big Data and analytics in transportation. The review shows the potential of Big Data and analytics to garner insights and improve transportation systems through the analysis of various forms of data obtained from traffic monitoring systems, connected vehicles, crowdsourcing, and social media. We discuss some platforms and software architecture for the transport domain, along with a wide array of storage, processing, and analytical techniques, and describe challenges associated with the implementation of Big Data and analytics. This review contributes broadly to the various ways in which cities can utilize Big Data in transportation to guide the creation of sustainable and safer traffic systems. Since research in Big Data and transportation is, by and large, at infancy, this article does not prescribe recommendations to the various challenges identified, which also constitutes the limitation of the article.

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