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1. Unmanned aerial vehicles as educational technology systems in construction engineering education

   https://doi.org/10.36680/j.itcon.2022.014  

   Mutis, Ivan; Antonenko, Pavlo (January 2022, Journal of Information Technology in Construction)

   Amor, Robert (Ed.)

   Integrating complex spatio-temporal cognitive tasks such as in-situ planning and trade coordination of job site activities is a continuous challenge to learners in Construction Engineering (CE) courses. Spatial information in this context addresses how physical resources are related to one another at a job site, whereas temporal information defines work sequences and hierarchies that transform physical resources. This paper discusses the impacts of using an innovative learning environment for supporting spatio-temporal cognition in CE education using aerial visualizations from Unmanned Aerial Vehicles (UAVs). Learners experience a unique, ‘birds-eye view’ of the spatio-temporal dynamics of a job site. The effects were on improved abilities to apply, analyze, and synthesize any form of design representation to situations and physical contexts. Our findings demonstrate that participants in the intervention group outperformed the control group on measures of learning and motivation, which underscores the potential of UAVs as an educational technology system in CE education. 

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2. Object Detectors for Construction Resources Using Unmanned Aerial Vehicles

   https://doi.org/10.1061/(ASCE)SC.1943-5576.0000598  

   Mutis, Ivan; Joshi, Virat Arun; Singh, Abhishek (November 2021, Practice Periodical on Structural Design and Construction)

   Project control operations in construction are mostly executed via direct observations and the manual monitoring of progress and performance of construction tasks on the job site. Project engineers move physically within job-site areas to ensure activities are executed as planned. Such physical displacements are error-prone and ineffective in cost and time, particularly in larger construction zones. It is critical to explore new methods and technologies to effectively assist performance control operations by rapidly capturing data from materials and equipment on the job site. Motivated by the ubiquitous use of unmanned aerial vehicles (UAVs) in construction projects and the maturity of computer-vision-based machine-learning (ML) techniques, this research investigates the challenges of object detection—the process of predicting classes of objects (specified construction materials and equipment)—in real time. The study addresses the challenges of data collection and predictions for remote monitoring in project control activities. It uses these two proven and robust technologies by exploring factors that impact the use of UAV aerial images to design and implement object detectors through an analytical conceptualization and a showcase demonstration. The approach sheds light on the applications of deep-learning techniques to access and rapidly identify and classify resources in real-time. It paves the way to shift from costly and time-consuming job-site walkthroughs that are coupled with manual data processing and input to more automated, streamlined operations. The research found that the critical factor to develop object detectors with acceptable levels of accuracy is collecting aerial images with for adequate scales with high frequencies from different positions of the same construction areas. 

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3. Contract-Theoretic Resource Control in Wireless Powered Communication Public Safety Systems

   https://doi.org/10.1109/GLOBECOM42002.2020.9348200  

   Patrizi, Nathan; Fragkos, Georgios; Tsiropoulou, Eirini Eleni; Papavassiliou, Symeon (December 2020, 2020 IEEE Global Communications Conference)

   null (Ed.)

   Recent technological advances in the use of Unmanned Aerial Vehicles (UAVs) and Wireless Powered Communications (WPC) have enabled the energy efficient operation of the Public Safety Networks (PSN) during disaster scenarios. In this paper, an energy efficient information flow and energy harvesting framework capturing users' risk-aware characteristics is introduced based on the principles of Contract Theory. To better support the operational effectiveness of the proposed framework, users are clustered in rescue groups following a socio-physical-aware group formation mechanism, while rescue leaders for each group are selected. A reinforcement learning approach is applied to enable the optimal matching between the UAVs and the rescue leaders in a distributed and efficient manner. The proposed contract-theoretic framework models the UAVs-victims relation based on a labor market setting via offering rewards to the users (incentives) in order to compensate them for their invested labor (reporting information). Detailed numerical results demonstrate the benefits and superiority of the proposed framework under different settings. 

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4. Towards More Accessible Open Source AI Platforms

   Murray, M; Motahar, T; Cakmak, M (July 2025, Proceedings of the 2025 Conference on Research on Equitable and Sustained Participation in Engineering, Computing, and Technology (RESPECT 2025))

   AI education is rapidly becoming essential as artificial intelligence transforms industries, yet students with disabilities often encounter significant barriers to learning and engagement. This paper examines accessibility challenges encountered by learners with visual, cognitive, and physical disabilities when using foundational tools for AI development. Using HuggingFace, an influential open-source platform, as a case study, we analyze barriers such as insufficient screen reader support, complex interfaces, and information overload. We propose design recommendations to promote equity and inclusivity in AI tools, aiming to empower diverse learners to thrive in AI education. Our work highlights the importance of inclusive design for CS educators, researchers, and policymakers. 

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5. Towards More Accessible Open Source AI Platforms

   Murray, M; Motahar, T; Cakmak, M (July 2025, Proceedings of the 2025 Conference on Research on Equitable and Sustained Participation in Engineering, Computing, and Technology (RESPECT 2025))

   AI education is rapidly becoming essential as artificial intelligence transforms industries, yet students with disabilities often encounter significant barriers to learning and engagement. This paper examines accessibility challenges encountered by learners with visual, cognitive, and physical disabilities when using foundational tools for AI development. Using HuggingFace, an influential open-source platform, as a case study, we analyze barriers such as insufficient screen reader support, complex interfaces, and information overload. We propose design recommendations to promote equity and inclusivity in AI tools, aiming to empower diverse learners to thrive in AI education. Our work highlights the importance of inclusive design for CS educators, researchers, and policymakers. 

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