Browsing by Author "Navon Cohen, Jaime"

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    Towards Native Code Offloading Platforms for Image Processing in Mobile Applications: A Case Study
    (IEEE, 2017) Valenzuela Gallegos, Guillermo Esteban; Neyem, Hugo Andres; Benedetto Causa, José Ignacio; Navon Cohen, Jaime; Sanabria Quispe, Pablo; Karmy, Juan A.; Balbontin, Felipe
    In this paper, we present a real-life case study to show the advantages of a new code offloading solution focused on improving both performance and energy consumption for image processing mobile applications. From our experiments, we found that offloading an image processing task would allow up to 5.7x speedup and 85% of reduction in energy consumption for low-end devices, and 1.7x speedup and 64.3% of reduction in energy consumption for high-end devices.
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    Understanding student interactions in capstone courses to improve learning experiences
    (2017) Neyem, Hugo Andrés; Diaz Mosquera, Juan Diego; Muñoz Gama, Jorge; Navon Cohen, Jaime
    Project-based courses can provide valuable learning experiences for computing majors as well as for faculty and community partners. However, proper coordination between students, stakeholders and the academic team is very difficult to achieve. We present an integral study consisting of a twofold approach. First, we propose a proven capstone course framework implementation in conjunction with an educational software tool to support and ensure proper fulfillment of most academic and engineering needs. Second, we propose an approach for mining process data from the information generated by this tool as a way of understanding these courses and improving software engineering education. Moreover, we propose visualizations, metrics and algorithms using Process Mining to provide an insight into practices and procedures followed during various phases of a software development life cycle. We mine the event logs produced by the educational software tool and derive aspects such as cooperative behaviors in a team, component and student entropy, process compliance and verification. The proposed visualizations and metrics (learning analytics) provide a multi-faceted view to the academic team serving as a tool for feedback on development process and quality by students