Hosted by CityU’s Center for Cybersecurity Innovation and School of Technology and Computing (STC), the Applied Research Symposium is open to faculty, professionals, and students from any discipline, university, or professional organization. At the end of each quarter, any students who would like to share their projects including capstone courses will present their outcomes. This symposium will provide opportunities for students, researchers, and practitioners to discuss the influence and impact of the applied computing on the future of our planet and our society
The online education system plays an essential role in the modern world. Education organizations and businesses want to provide their assessment system based on an online education system. During the online training, GBs of data is continuously generating and being collected from the user every event. Those valuable data can be processed and analyzed to support many decisions in a second such as user state, chatting detection, and online help desk. In addition, demand from different roles, such as data analysts, data science, executives, that access to those data has increased recently. In this paper, we present the new architecture for real-time data streaming based on the existing education system and identify three challenges that need to be addressed in our architecture. First, most organizations or education areas rely on the open-source system for their learning system. Second, on top of the synchronized learning management system, we add customized solutions for improvement and customization to give real-time ability to meet the synchronized online test requirement.
for the current situation including availability, cost, technologies.
Nowadays the major problem in the world is the increased number of road accidents. The main reason for the accident is the Driver’s sleepiness or lack of concentration. Research in driver drowsiness observing may help to reduce accidents. This paper, therefore, proposes a facial expression approach for implementing a driver’s drowsiness alert system for old vehicles which would detect and monitor the yawning and sleepiness of the driver. We use a facial landmark detector using Dlib pre-trained model to extract face and facial landmark, followed by calculation of Eye Aspect Ratio (EAR), and Mouth Aspect Ratio (MAR) to detect whether a driver is concentrated into driving. Intel’s Open-source Image processing libraries (OPENCV) are used as a primary image processing tool. EAR is analyzed by analyzing Euclidean distance between measured eye coordinates. Flask is used to create a web application and create a warning system for the drowsy driver for safety purposes. This experimental result shows better real-time performance than traditional methods.
The purpose of this project is to suggest adopting business analytics and cloud services to improve existing Unmanned Aerial Vehicle Maintenance.