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1. SpaceMediator: Leveraging Authorization Policies to Prevent Spatial and Privacy Attacks in Mobile Augmented Reality

   https://doi.org/10.1145/3589608.3593839  

   Claramunt, Luis ; Rubio-Medrano, Carlos ; Baek, Jaejong ; Ahn, Gail-Joon ( May 2023 , SACMAT '23: Proceedings of the 28th ACM Symposium on Access Control Models and Technologies)

   Mobile Augmented Reality (MAR) is a portable, powerful, and suitable technology that integrates digital content, e.g., 3D virtual objects, into the physical world, which not only has been implemented for multiple intents such as shopping, entertainment, gaming, etc., but it is also expected to grow at a tremendous rate in the upcoming years. Unfortunately, the applications that implement MAR, hereby referred to as MAR-Apps, bear security issues, which have been imaged in worldwide incidents such as robberies, which has led authorities to ban MAR-Apps at specific locations. Existing problems with MAR-Apps can be classified into three categories: first, Space Invasion, which implies the intrusive modification through MAR of sensitive spaces, e.g., hospitals, memorials, etc. Second, Space Affectation, which involves the degradation of users' experience via interaction with undesirable MAR or malicious entities. Finally, MAR-Apps mishandling sensitive data leads to Privacy Leaks. To alleviate these concerns, we present an approach for Policy-Governed MAR-Apps, which allows end-users to fully control under what circumstances, e.g., their presence inside a given sensitive space, digital content may be displayed by MAR-Apps. Through SpaceMediator, a proof-of-concept MAR-App that imitates the well-known and successful MAR-App Pokemon GO, we evaluated our approach through a user study with 40 participants, who recognized and prevented the issues just described with success rates as high as 92.50%. Furthermore, there is an enriched interest in Policy-Governed MAR-Apps as 87.50% of participants agreed with it, and 82.50% would use it to implement content-based restrictions in MAR-Apps These promising results encourage the adoption of our solution in future MAR-Apps. 

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2. Mobile augmented reality supporting families’ immersive collaborative learning: Learning-on-the-move for place-based geoscience sense-making

   https://doi.org/10.1007/s11412-023-09399-9  

   Zimmerman, Heather Toomey ; Land, Susan M. ; Faimon, Lillyanna ; Chiu, Yu-Chen ( July 2023 , International Journal of Computer-Supported Collaborative Learning)

   We investigated how families experienced immersion as they collaboratively made sense of geologic time and geoscience processes during a place-based, learning-on-the-move (LOTM) experience mediated by a mobile augmented reality (MAR) app. Our team developed an MAR app,Time Explorers, that focused on how rock-water interactions shaped Appalachia over millions of years. Data were collected at the Children’s Garden at the Arboretum at Penn State. Data sources were videos of app usage, point-of-view camera recordings with audio capturing family conversations, and interviews from 17 families (51 people). The analytical technique was interaction analysis, in which episodes of family sense-making were identified and developed into qualitative vignettes focused on how immersion did or did not support learning about geoscience and geologic time. We analyzed how design elements supported sensory, actional, narrative, and social immersion through photo-taking, discussion prompts, and augmented reality visualizations. Findings showed that sensory and social immersion supported sense-making conversations and observational inquiry, while narrative and actional immersion supported deep family engagement with the geoscience content. At many micro-sites of learning, families engaged in multiple immersive processes where conversations, observational inquiry, and deep engagement with the geoscience came together during LOTM. This analysis contributes to the CSCL literature on theory related to LOTM in outdoor informal settings, while also providing design conjectures in an immersive, family-centered, place-based LOTM framework.

    

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3. Using Digital Sketching and Augmented Reality Mobile Apps to Improve Spatial Visualization in a Freshmen Engineering Course

   https://doi.org/10.18260/1-2--33501  

   Bairaktarova, Diana ; Van Den Einde, Lelli ; Bell, John ( June 2019 , 2019 ASEE Annual Conference & Exposition)

   null (Ed.)

   Spatial reasoning skills contribute to performance in many STEM fields. For example, drawing sectional views of three-dimensional objects is an essential skill for engineering students. There is considerable variation in the spatial reasoning skills of prospective engineering students, putting some at risk for compromised performance in their classes. This study takes place in a first-year engineering Spatial Visualization course to integrate recent practices in engineering design education with cognitive psychology research on the nature of spatial learning. We employed three main pedagogical strategies in the course - 1) in class instruction on sketching; 2) spatial visualization training; and 3) manipulation of physical objects (CAD/3D print creations). This course endeavors to use current technology, online accessibility, and implementation of the three pedagogical strategies to bring about student growth in spatial reasoning. This study is designed to determine the effect of adding two different spatial reasoning training apps to this environment. Over 230 students (three sections) participated in our study. In two of the three sections, students received interactive spatial visualization training using either a spatial visualization mobile touchscreen app in one section or an Augmented Reality (AR) app in the other section. Research suggests that there are benefits to using the Spatial Vis Classroom mobile app for college students.The app has been shown to increase student persistence resulting in large learning gains as measured by the Purdue assessment of spatial visualization (PSVT-R), especially for students starting with poor spatial visualization skills. The Spatial Vis Classroom app can be used in the classroom or assigned as homework. The AR app is designed to help users develop their mental rotation abilities. It is designed to support a holistic understanding of 3-dimensional objects, and research has shown that, in combination with a traditional curriculum, it increases students’ abilities also measured by the PSVT-R. Of particular interest, the data suggest that the app overcomes the advantage found by males over females in a traditional class alone focused on spatial reasoning. Both of the course sections were required to use the apps for approximately the same time in class and outside of class. Students in the control section were required to do hand sketching activities in class and outside of class, with roughly the same completion time as for the sections with the apps. Students grades were not affected by using the three different approaches as grading was based on completion only. Based on current literature, we hypothesize that overall benefits (PSVT-R gains) will be comparable across the 3 treatments but there will be different effects on attitude and engagement (confidence,enjoyment, and self-efficacy). Lastly, we hypothesize that the treatments will have different effects on male/female and ethnic categories of the study participants. The final paper will include an analysis of results and a report of the findings. 

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4. Comparing Classifiers: A Look at Machine-Learning and the Detection of Mobile Malware in COVID-19 Android Mobile Applications

   https://doi.org/10.1145/3565287.3617629  

   Johnson, Seth ; Donner, Ray ; Perez, Alfredo J. ( October 2023 , MobiHoc '23: Proceedings of the Twenty-fourth International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing)

   The COVID-19 pandemic was a catalyst for many different trends in our daily life worldwide. While there has been an overall rise in cybercrime during this time, there has been relatively little research done about malicious COVID-19 themed AndroidOS applications. With the rise in reports of users falling victim to malicious COVID-19 themed AndroidOS applications, there is a need to learn about the detection of malware for pandemics-themed mobile apps.. In this project, we extracted the permissions requests from 1959 APK files from a dataset containing benign and malware COVID-19 themed apps. We then created and compared eight unique models of four varying classifiers to determine their ability to identify potentially malicious APK files based on the permissions the APK file requests: support vector machine, neural network, decision trees, and categorical naive bayes. These classifiers were then trained using Synthetic Minority Oversampling Technique (SMOTE) to balance the dataset due to the lack of samples of malware compared to non-malware APKs. Finally, we evaluated the models using K-Fold Cross-Validation and found the decision tree classifier to be the best performing classifier. 

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5. SmartDet: Context-Aware Dynamic Control of Edge Task Offloading for Mobile Object Detection

   https://doi.org/10.1109/WoWMoM54355.2022.00034  

   Callegaro, Davide ; Levorato, Marco ; Restuccia, Francesco ( June 2022 , IEEE WoWMoM)

   Mobile devices such as drones and autonomous vehicles increasingly rely on object detection (OD) through deep neural networks (DNNs) to perform critical tasks such as navigation, target-tracking and surveillance, just to name a few. Due to their high complexity, the execution of these DNNs requires excessive time and energy. Low-complexity object tracking (OT) is thus used along with OD, where the latter is periodically applied to generate "fresh" references for tracking. However, the frames processed with OD incur large delays, which does not comply with real-time applications requirements. Offloading OD to edge servers can mitigate this issue, but existing work focuses on the optimization of the offloading process in systems where the wireless channel has a very large capacity. Herein, we consider systems with constrained and erratic channel capacity, and establish parallel OT (at the mobile device) and OD (at the edge server) processes that are resilient to large OD latency. We propose Katch-Up, a novel tracking mechanism that improves the system resilience to excessive OD delay. We show that this technique greatly improves the quality of the reference available to tracking, and boosts performance up to 33%. However, while Katch-Up significantly improves performance, it also increases the computing load of the mobile device. Hence, we design SmartDet, a low-complexity controller based on deep reinforcement learning (DRL) that learns to achieve the right trade-off between resource utilization and OD performance. SmartDet takes as input highly-heterogeneous context-related information related to the current video content and the current network conditions to optimize frequency and type of OD offloading, as well as Katch-Up utilization. We extensively evaluate SmartDet on a real-world testbed composed by a JetSon Nano as mobile device and a GTX 980 Ti as edge server, connected through a Wi-Fi link, to collect several network-related traces, as well as energy measurements. We consider a state-of-the-art video dataset (ILSVRC 2015 - VID) and state-of-the-art OD models (EfficientDet 0, 2 and 4). Experimental results show that SmartDet achieves an optimal balance between tracking performance – mean Average Recall (mAR) and resource usage. With respect to a baseline with full Katch-Up usage and maximum channel usage, we still increase mAR by 4% while using 50% less of the channel and 30% power resources associated with Katch-Up. With respect to a fixed strategy using minimal resources, we increase mAR by 20% while using Katch-Up on 1/3 of the frames. 

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