Conference Agenda

Learning processes in Computer Science programs require the development of transversal competencies that are often excluded from official curricula. The infusion of storytelling in teaching strategies can be used to approach these competencies, while supporting meaningful learning. Previous studies have shown the multifaceted role of storytelling in bridging technical knowledge, cultural context, and user engagement across diverse fields. This paper reports on our experience using storytelling in an undergraduate database course. Namely, storytelling was used to evaluate the physical organization of files and indexes. The purpose of this strategy was addressing subject-specific contents while fostering students’ creativity and communication competencies. In this paper we describe the design and implementation of our storytelling-infused teaching strategy, and provide samples of students’ learning results as well as lessons learned from the authors. An anonymous student survey was used to assess this strategy. Overall, results from the survey show that most of the students considered that the teaching strategy helped them develop their creativty and communication skills, and allowed further integration in teamwork dynamics. Indeed, students were able to clearly express these competencies through the creation and presentation of complex characters and conflicts in entertaining stories. Most students also found the strategy to be fun and enjoyable, but nevertheless stressful, due to the short time they had to create the story, while dealing with work overloads from other courses. Furthermore, some students expressed their frustration with the strategy, as they failed to see the value of nurturing creativity through storytelling in the field of computer science. As future work, we plan on experimenting with storytelling-infused teaching strategies in other computer science courses, to ascertain at what level of the computer science program these strategies may yield the best results for student learning and motivation.

Digital Twin (DT) technology has gained popularity in the science and tech industry. This research explores how Digital twin(DT) technology combined with anomaly detection can enhance the reliability of the Electric Vehicle (EV). In the paper, the authors provide an analysis of the methods and tools that are implemented in existing DT for EV, which has shown that anomaly detection could improve functionality of the DT and robustness of the EV. A modular approach and Model based design techniques were used by the authors. For the anomaly detection Failure Mode Effect Analysis was used. The anomaly detection algorithm for Open Modular Experimental Electrical Vehicle (OMAX EV) was developed, which allows to reach an accuracy 83%.

This work considers the potential of band linear system solvers

for improving the scalability of the Extreme Learning Machine method

at large model sizes. The model is tested on the MNIST dataset with

a range of solvers provided by the SciPy Python library. The results

present an overall performance, the performance impact of band solvers

across different matrix bandwidths, and the performance versus runtime

analysis. The findings show potential in applying the proposed method

to very large ELM models with narrow band matrices.

A key challenge in Industry 4.0 is integrating advanced technologies to enhance overall system efficiency. While collaborative robots (cobots) and deep learning-based object detection models have advanced, their deployment for vision-based tasks with robotic arms remains understudied. In this research, a vision-set mounted on a robotic arm is tested for sorting the mechanical fasteners. Three object detection models i.e., YOLO, SSD, and Faster R-CNN have been trained on over 2500 images and their sorting performance is evaluated for static and real-time object detection using vision-set. The trained models were validated through deployment with robotic arm. YOLO has proven to be the most effective algorithm considering training, speed and accuracy while the other models lacked in certain aspects one way or the other.