Department of Computer Science

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

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Assessing learning in an immersive virtual reality: a curriculum-based experiment in chemistry education
(2024) Qorbani, Sam; Dalili, Shadi; Arya, Ali; Joslin, Christopher
Despite the recent advances in Virtual Reality technology and its use in education, the review of the literature shows several gaps in research on how immersive virtual environments impact the learning process. In particular, the lack of curriculum-specific experiments along with investigations of the effects of different content, activity, and interaction types in the current VR studies has been identified as a significant shortcoming. This has been more significant in STEM fields, where VR has the potential to offer engaging experiential learning opportunities. The study reported here was designed to address this gap by assessing the effect of authentic visualization and interaction types on learning a particular scientific concept. A use case scenario of “orbital hybridization” in chemistry education was selected to create this experiment and to collect data for analysis. We collected data on learning outcomes, task-completion efficiency, accuracy, and subjective usability. A combination of learning content and tasks designed based on the relevant educational theories was presented to three groups: 2D, VR interaction type 1 (hand gestures), and VR interaction type 2 (ray casting). The results showed that VR could improve learning and that interaction type could influence efficiency and accuracy depending on the task.
Statistical privacy protection for secure data access control in cloud
(2024) Baseri, Yaser; Hafid, Abdelhakim; Daghmehchi Firoozjaei, Mahdi; Cherkaoui, Soumaya; Ray, Indrakshi
Cloud Service Providers (CSPs) allow data owners to migrate their data to resource-rich and powerful cloud servers and provide access to this data by individual users. Some of this data may be highly sensitive and important and CSPs cannot always be trusted to provide secure access. It is also important for end users to protect their identities against malicious authorities and providers, when they access services and data. Attribute-Based Encryption (ABE) is an end-to-end public key encryption mechanism, which provides secure and reliable fine-grained access control over encrypted data using defined policies and constraints. Since, in ABE, users are identified by their attributes and not by their identities, collecting and analyzing attributes may reveal their identities and violate their anonymity. Towards this end, we define a new anonymity model in the context of ABE. We analyze several existing anonymous ABE schemes and identify their vulnerabilities in user authorization and user anonymity protection. Subsequently, we propose a Privacy-Preserving Access Control Scheme (PACS), which supports multi-authority, anonymizes user identity, and is immune against users collusion attacks, authorities collusion attacks and chosen plaintext attacks. We also propose an extension of PACS, called Statistical Privacy-Preserving Access Control Scheme (SPACS), which supports statistical anonymity even if malicious authorities and providers statistically analyze the attributes. Lastly, we show that the efficiency of our scheme is comparable to other existing schemes. Our analysis show that SPACS can successfully protect against Collision Attacks and Chosen Plaintext Attacks.
Parent process termination: an adversarial technique for persistent malware
(2023) Daghmehchi Firoozjaei, Mahdi; Samet, Saeed; Ghorbani, Ali A.
Persistent malware use techniques, such as obfuscation, process injection, and system call abuse to evade security mechanisms and avoid detection throughout their compromise. Malware analysis and memory forensics must have proper skill for fighting them. To show the limitation of current memory forensics, we introduce an adversarial technique to remove the forensics evidence required to identify malware, called parent process termination (PPT). PPT neither creates a new malware nor does it manipulate the features of a running process like malware obfuscation techniques, which abuse the parent–child relationship. In PPT, the malware process creates child processes for a malicious purpose and then terminates. This termination, letting the operating system (OS) reuses the parent process’s resources and thus erases all trace of it, while leaving its children to perform anomalous activities. To show PPT’s applicability in Windows OS, we run and analyze selected malware samples in a controlled environment. We implement PPT and show how this technique benefits from current memory forensics tools being unable to identify the exited processes. The forensics analysis proves behaviour of the PPT adversarial technique run in different malware executions. Our experiments show PPT successfully removes forensics evidence to identify the source of malicious activity. We hope these results can shed light on the future design of memory forensics tools and better-informed choices by users.
On slicing weighted energy-harvesting wireless sensing networks with transmission range uncertainty
(2022) Abougamila, Salwa; Elmorsy, Mohammed; Elmallah, Ehab S.
In this paper, we deal with a wireless sensor network (WSN) infrastructure management problem where a provider wants to partition a network into a given number of node-disjoint subgraphs (called slices) for running different user applications. Nodes in the given infrastructure use energy harvesting for prolonged service time. The nodes manage fluctuations in their stored energy by adjusting their transmission range. We assume that each node is assigned an importance weight, and model the overall network using a probabilistic graph. In this context, we formalize a problem, denoted k-WBS-RU (for k weighted balanced slices with range uncertainty), to partition the network into k slices subject to some connectivity and operation constraints. We devise a solution to the problem, and present numerical results on the quality of the obtained slices. We also discuss an application of the proposed framework and solution when the assigned weights are derived from an area coverage application.
Flow sharing reliability in energy harvesting wireless sensing networks
(2024) Abougamila, Salwa; Elmorsy, Mohammed; Elmallah, Ehab S.
This paper introduces a new resource sharing problem in wireless sensor networks (WSNs) that employ energy harvesting for prolonged network uptime. The problem is on managing a given infrastructure of EH-WSNs by supporting concurrent applications. Each application is characterized by a set of traffic generating nodes, a sink node, and a minimum required traffic rate that should be periodically delivered to its sink node. The overall EH-WSN is modelled by a probabilistic graph where energy fluctuation over time in each node is described by a probability distribution and handled by adjusting the flow relaying capacity of a node. Performance of the obtained network management scheme is assessed by a reliability metric on the formulated probabilistic graph. We call the formulated problem the flow sharing reliability (FS-REL) problem in EH-WSNs. We present a heuristic algorithm to cope with the problem using ideas from minimum cost multi-commodity flows in networks and approximation of flow reliability using a factoring algorithm. We also present numerical results that give more insights into the problem and the proposed solution.
Analysis of hockey forward line Corsi: should the focus be on forward pairs?
(2024) Brownlee, Samuel; Khan, Ayesha; Vanderzyl, Barnaby; El-Hajj, Mohamad
Professional ice hockey is a popular sport in North America, with multiple previous analyses providing insights into teams. Most research has been done on analyzing pairs of players on the same team that work well together. The focus of this study was to analyze if trios on a forward line perform well together, as there has not been enough research in this field. Our goal was to determine if the third player changes the performance of a duo and identify key factors that explain this change. We have analyzed more than 14 years worth of data. This data started with more than 100 dimensions; from those 100, 35 dimensions were chosen for analysis. To reach our conclusion, we used three methods: K-Means, Random Forest, and Support vector machines. Single variate random forest was used to analyze which variables affected the Corsi Percentage. The results from K-Mean clustering, combined with the results from Single Variate Random Forest, were used to see if the substitution of a third player on a line of three makes a difference in the overall performance of the line. The Support Vector Machine algorithm was used to reinforce the cluster numbers obtained from K-means clustering. Our study found that adding a third player will have a positive effect when the third player consistently plays with the other two players and the three players participate more effectively in defence. These findings could help teams plan how they form their player lines when they want to achieve good game results.
Mining COVID-19 data to predict the effect of policies on severity of outbreaks
(2023) El-Hajj, Mohamad; Anton, Calin; Anton, Cristina; Dobosz, Dominic; Smith, Iain; Deiab, Fattima; Saleh, Nagam
During the years 2020, 2021, and partially 2022, the COVID-19 virus ran rampant across the globe, causing devastating effects on the masses. Using data mining techniques, we explored factors linked to severe cases of COVID-19 and tried to identify the effect of different government policies on the evolution of the severity of infections. Four countries were selected with a date range of the year 2021 to investigate each region’s efforts regarding vaccine distribution and specific policies enacted for COVID-19 suppression. Pearson’s Correlation Coefficients were used to help establish initially relationships between the policies, vaccines, and severe cases. We used the identified factors to predict the number of new COVID-19 cases and hospital ICU admissions. We included all the country data from Our World in Data (OWID) for this phase. Our investigation indicates that, given enough data, long-range trend predictions can be obtained using Random Forest Regressors. A trained Random Forest model can readily explain factors that effectively slow the spread of COVID-19. With proposed policies given as input, the model can return the expected number of cases, thus informing policies without spending multiple weeks tracking results.
Statistical privacy protection for secure data access control in cloud
(2024) Baseri, Yaser; Hafid, Abdelhakim; Daghmehchi Firoozjaei, Mahdi; Cherkaoui, Soumaya; Ray, Indrakshi
Cloud Service Providers (CSPs) allow data owners to migrate their data to resource-rich and powerful cloud servers and provide access to this data by individual users. Some of this data may be highly sensitive and important and CSPs cannot always be trusted to provide secure access. It is also important for end users to protect their identities against malicious authorities and providers, when they access services and data. Attribute-Based Encryption (ABE) is an end-to-end public key encryption mechanism, which provides secure and reliable fine-grained access control over encrypted data using defined policies and constraints. Since, in ABE, users are identified by their attributes and not by their identities, collecting and analyzing attributes may reveal their identities and violate their anonymity. Towards this end, we define a new anonymity model in the context of ABE. We analyze several existing anonymous ABE schemes and identify their vulnerabilities in user authorization and user anonymity protection. Subsequently, we propose a Privacy-Preserving Access Control Scheme (PACS), which supports multi-authority, anonymizes user identity, and is immune against users collusion attacks, authorities collusion attacks and chosen plaintext attacks. We also propose an extension of PACS, called Statistical Privacy-Preserving Access Control Scheme (SPACS), which supports statistical anonymity even if malicious authorities and providers statistically analyze the attributes. Lastly, we show that the efficiency of our scheme is comparable to other existing schemes. Our analysis show that SPACS can successfully protect against Collision Attacks and Chosen Plaintext Attacks.
Mining COVID-19 data to predict the effect of policies on severity of outbreaks
(2023) El-Hajj, Mohamad; Anton, Calin; Anton, Cristina; Dobosz, Dominic; Smith, Iain; Deiab, Fattima; Saleh, Nagam
During the years 2020, 2021, and partially 2022, the COVID-19 virus ran rampant across the globe, causing devastating effects on the masses. Using data mining techniques, we explored factors linked to severe cases of COVID-19 and tried to identify the effect of different government policies on the evolution of the severity of infections. Four countries were selected with a date range of the year 2021 to investigate each region’s efforts regarding vaccine distribution and specific policies enacted for COVID-19 suppression. Pearson’s Correlation Coefficients were used to help establish initially relationships between the policies, vaccines, and severe cases. We used the identified factors to predict the number of new COVID-19 cases and hospital ICU admissions. We included all the country data from Our World in Data (OWID) for this phase. Our investigation indicates that, given enough data, long-range trend predictions can be obtained using Random Forest Regressors. A trained Random Forest model can readily explain factors that effectively slow the spread of COVID-19. With proposed policies given as input, the model can return the expected number of cases, thus informing policies without spending multiple weeks tracking results.
Hy-bridge: a hybrid blockchain for privacy-preserving and trustful energy transactions in Internet-of-Things platforms
(2020) Daghmehchi Firoozjaei, Mahdi; Ghorbani, Ali; Kim, Hyoungshick; Song, JaeSeung
In 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.
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; Daghmehchi Firoozjaei, Mahdi
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 W.; 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.

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