Department of Computer Science

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

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Automatic deep learning segmentation of the hippocampus on high resolution diffusion MRI and its application to the healthy lifespan
(2024) Efird, Cory; Neumann, Samuel; Solar, Kevin; Beaulieu, Christian; Cobzas, Dana; Miller, Dylan
Diffusion tensor imaging (DTI) can provide unique contrast and insight into microstructural changes with age or disease of the hippocampus, although it is difficult to measure the hippocampus because of its comparatively small size, location, and shape. This has been markedly improved by the advent of a clinically feasible 1-mm isotropic resolution 6-min DTI protocol at 3 T of the hippocampus with limited brain coverage of 20 axial-oblique slices aligned along its long axis. However, manual segmentation is too laborious for large population studies, and it cannot be automatically segmented directly on the diffusion images using traditional T1 or T2 image-based methods because of the limited brain coverage and different contrast. An automatic method is proposed here that segments the hippocampus directly on high-resolution diffusion images based on an extension of well-known deep learning architectures like UNet and UNet++ by including additional dense residual connections. The method was trained on 100 healthy participants with previously performed manual segmentation on the 1-mm DTI, then evaluated on typical healthy participants (n = 53), yielding an excellent voxel overlap with a Dice score of ~ 0.90 with manual segmentation; notably, this was comparable with the inter-rater reliability of manually delineating the hippocampus on diffusion magnetic resonance imaging (MRI) (Dice score of 0.86). This method also generalized to a different DTI protocol with 36% fewer acquisitions. It was further validated by showing similar age trajectories of volumes, fractional anisotropy, and mean diffusivity from manual segmentations in one cohort (n = 153, age 5–74 years) with automatic segmentations from a second cohort without manual segmentations (n = 354, age 5–90 years). Automated high-resolution diffusion MRI segmentation of the hippocampus will facilitate large cohort analyses and, in future research, needs to be evaluated on patient groups.
Explaining anatomical shape variability: supervised disentangling with a variational graph autoencoder
(2023) Kiechle, Johannes; Miller, Dylan; Slessor, Jordan; Pietrosanu, Matthew; Kong, Linglong; Beaulieu, Christian; Cobzas, Dana
This work proposes a modular geometric deep learning framework that isolates shape variability associated with a given scalar factor (e.g., age) within a population (e.g., healthy individuals). Our approach leverages a novel graph convolution operator in a variational autoencoder to process 3D mesh data and learn a meaningful, low-dimensional shape descriptor. A supervised disentanglement strategy aligns a single component of this descriptor with the factor of interest during training. On a toy synthetic dataset and a high-resolution diffusion tensor imaging (DTI) dataset, the proposed model is better able to disentangle the learned latent space with a simulated factor and patient age, respectively, relative to other state-of-the-art methods. The relationship between age and shape estimated in the DTI analysis is consistent with existing neuroimaging literature.
Scrum package for Software Engineering education
(2025) Pang, Candy
Nowadays, software is embedded in almost all devices (e.g., car, refrigerator, kettle). Creating high-quality software is extremely important now and in the future. The process of creating high-quality software is called Software Engineering (SE). As far as I know, every undergraduate Computer Science (CS) curriculum includes SE education. Agile is the most used SE methodology in the IT industry, and Scrum is the most popular Agile framework. Therefore, most SE courses in the CS undergraduate curriculum teach Scrum through software-developing course projects to prepare students for their careers. One challenge in teaching Scrum is its lack of a definitive definition. In the IT industry, organizations customize the Scrum framework according to their needs without restriction. Therefore, teachers must have in-depth Scrum experience to teach students how to employ Scrum when dealing with various software development challenges. Through these challenges, teachers guide students to understand the pros and cons of the Scrum framework and learn how to apply Scrum in different organizations under different situations. Teachers having in-depth Scrum experience are essential in undergraduate SE education. However, many teachers, including instructors and teaching assistants (TAs), lack industrial Scrum experience. As a technical architect consultant in the IT industry for over 15 years, who participated in over 20 projects in more than 10 organizations, mainly using Scrum, I created a Scrum teaching package for my single-term Introduction to Software Engineering (Intro-SE) course. The package includes Scrum process templates, template instructions, common mistake descriptions, evaluation schemas, and feedback suggestions. With the package, teachers can teach Scrum professionally without industrial experience. I used the teaching package in Intro-SE for six terms through three years and enhanced the package to improve students' learning experience. I describe the package's content, usage, and benefits in this paper. A Scrum project starts with defining a product backlog. Then, the project is split into sprints of a few weeks each. For the project, the package included a product backlog template. For the sprints, the package consists of sprint backlog template, sprint planning template, sprint task board template, sprint burndown chart template, sprint tracking template, sprint retrospective template, and sprint demo template. The package also includes detailed template evaluation schemas with feedback suggestions that enforce positive behaviors and recommend improvements. With the package, the teachers may mark students' submissions according to the industrial practices with meaningful feedback. This paper also points out significant shortcomings and downfalls of the students so that the teachers may be more aware of the learning opportunities, guide the students to learn from their mistakes, and apply remediations.
Developing a simple and cost-effective markerless augmented reality tool for chemistry education
(2021) Qorbani, Sam; Abdinejad, Maryam; Ferrag, Celia; Dalili, Shadi
Traditional visualization methods have a limited capacity to enhance students’ understanding of 3D molecular structure and reactivity. Studies have shown that 3D visualization tools can play an essential role in improving students’ learning. Augmented reality (AR) is a technology that merges virtual objects with real-world images seamlessly. We have previously developed a “marker-based” AR app (ARchemy) for Android devices to help students visualize molecular structures in 3D. Using recent technological advancements, to avoid the limitation of using a printed marker, we have successfully developed a simple and low-cost “markerless” AR app, which can be used for both Android and iOS devices. Students who used the AR app saw a significant increase in understanding of the complexity of molecular structures compared to those who used traditional molecular modeling kits. This unique technology will not only help teachers create more interactive and engaging lessons but also benefit students by making it more accessible and cost-effective to access the resources from any place and at any time.
ScienceVR: a virtual reality framework for STEM education, simulation and assessment
(2021) Qorbani, Sam; Arya, Ali; Nowlan, Nuket; Abdinejad, Maryam
This paper addresses the use of Virtual Reality (VR) in Science, Technology, Engineering, and Math (STEM) education. There are limited studies investigating the proper design and effectiveness of VR in STEM education, and current VR frameworks and applications lack explicit links to the established learning theories and assessment mechanisms to evaluate learning outcomes. We present ScienceVR, an educational virtual reality design framework, illustrated through a science laboratory prototype, to bridge some of the gaps identified in the design and development of a VR environment for learning. We established design guidelines and implemented an in-app data collection system to measure users’ learning, performance, and task completion rate. Our evaluation using ANOVA and other non-parametric methods with 36 participants in three groups: immersive VR (IVR), desktop VR(DVR), and 2D indicated improved usability and learning outcomes for the IVR group. Task completion rate in the IVR group was higher (68% compared to DVR with 50%). For memorability, the IVR condition performed better than DVR while for learnability, IVR&DVR performed significantly better than 2D. IVR group has performed better and faster with more accuracy compared to the DVR group in completing the tasks.
Improving accessibility of elevation control in an immersive virtual environment
(2022) Qorbani, Sam; Abdinejad, Maryam; Arya, Ali; Joslin, Chris
Despite the advances made in Virtual Reality (VR) technology, the design of VR experiences lacks sufficient focus on accessibility and inclusion as the primary requirements. These are especially important for STEM education, where engaging in experiential activities is essential. This study was conducted to investigate accessibility considerations in the design and development of Immersive VR (IVR) learning spaces for wheelchair users. The specific research question is: How can we make a VR system easier to interact with for wheelchair users needing vertical movement? A user study with thirty (30) participants in three groups was conducted: Group A (the control group, non-wheelchair users) who used natural body movement to interact with the environment, Group B (verification group, non-wheelchair users) who used software controls for accessibility, and Group C (wheelchair users) who used the same software accessibility feature. The results indicate that the accessibility feature enabled wheelchair users to complete the tasks requiring raising or lowering of the body, with almost similar levels of completion rate and accuracy.
Using virtual reality to improve STEM education
(2023) Qorbani, Sam; Arya, Ali; Joslin, Chris
The purpose of this dissertation is to investigate the effect of using Virtual Reality (VR) technology on the students' experience in science education, particularly for interaction, learning, and accessibility. Science, Technology, Engineering, and Math (STEM) education has special requirements such as lab-based activities and abstract concepts that complicate setting up the environment and learning process. These complications are increased due to the pandemic and other remote access requirements. VR has unique affordances that make it a promising solution but its use in this regard has not been properly investigated and there are many open research questions related to its effect on interaction, learning, and accessibility in STEM education. Focusing on these three aspects, we ran a series of quantitative and qualitative studies to find out if the use of VR in science labs leads to an increased level of learning, efficiency, and accuracy of the tasks (measured by pre-post knowledge tests and the in-app data collection system). The Immersive/head-mounted VR (IVR) was compared to Desktop VR (DVR) and 2D/text-based conditions. Results indicated a significant difference in some areas particularly related to post-knowledge score, spatial skills, and learnability between 2D and VR conditions. Task completion rate, efficiency, and accuracy also indicated a significant difference between IVR and DVR groups, showing IVR performing better. The qualitative evaluation included an analysis of students' experiences with the use of VR and their perspective on its application for education. The result indicated that in areas such as ease of use, learnability, engagement, and overall satisfaction they preferred VR treatment better compared to 2D/ Text as they found the use of VR to be beneficial to their learning. Our studies also showed the efficacy of software-based accessibility features that improved interaction and learning for wheelchair users. We concluded that the implementation of a virtual environment for STEM education requires careful considerations in the design and implementation to make it technically practical to run on mobile Head Mounted Displays (HMD), be relevant based on established learning theories, minimize the effect of cybersickness, and be accessible for a wider range of audience.
Higher-order thinking skills assessment in 3D virtual learning environments using motifs and expert data
(2023) Qorbani, Sam; Nowlan, Nuket; Arya, Ali; Abdinejad, Maryam
The research reported in this paper addresses the problem of assessing higher-order thinking skills, such as reflective and creative thinking, within the context of virtual learning environments. Assessment of these skills requires process-based observations and evaluation, as the output-based methods have been found to be insufficient. Virtual learning environments offer a wealth of data on the process, which makes them good candidates for process-based evaluation, but the existing assessment methods in these environments have shortcomings, such as reliance on large data sets, inability to offer specific feedback on actions, and the lack of consideration for how actions are integrated into bigger tasks. Demonstrating and confirming the ability of three-dimensional virtual learning environments to work with process metrics for assessment, we propose and evaluate the use of motifs as an assessment tool. Motifs are short and meaningful combination of metrics. Combining time-ordered motifs with a similarity analysis between expert and learner data, our proposed approach can potentially offer feedback on specific actions that the learner takes, as opposed to single output-based feedback. It can do so without the use of large training datasets due to reliance on expert data and similarity analysis. Through a user study, we found out that such a motif-based approach can be effective in the assessment of higher-order thinking skills while addressing the identified shortcomings of previous work. We also address the limited research on similarity-based analysis methods, compare their effectiveness, and show that utilizing different similarity measures for different tasks may be a more effective approach. Our proposed method facilitates and encourages the involvement of instructors and course designers through the definition of motifs and expert problem-solving paths.
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.

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