Application of augmented reality in aviation: improving engagement of cabin crew during emergency procedures training

The main duty of the cabin crew is to ensure the safety of all passengers onboard and are crucial during emergency situations. It is mandatory for cabin crew to attend the annual Emergency Procedures Training (EPT) to be able to operate as cabin crew regardless of the seniority. Despite the high importance, this training can be long and bulky leading to boredom and lack of engagement, hence jeopardizing the importance of in-flight safety procedures. Although Augmented Reality (AR) can potentially address this issue while enhancing engagement and learning retention, limited work has been undertaken to apply this technology to EPT. As such, this paper investigates whether augmented reality can effectively improve user engagement during emergency procedures training in the context of aviation. In this endeavor, an AR-based application was developed and is presented in this paper. The Positive Engagement Evaluation Method (PEEM) was then used to assess engagement among the 45 cabin-crew of the national carrier. From the PEEM matrix, the positive engagement score obtained was 10.58 and mean scores from the questionnaire ranged from 3 to 4.7. This highlights that Augmented Reality has the potential to enhance the motivation and engagement of users during the emergency procedures training, although a few limitations were identified

Using tangible user interfaces for teaching concepts of internet of things: usability and learning effectiveness

Purpose This paper aims to explore the use of tangible user interfaces for teaching concepts related to internet of things by focusing on two aspects, notably, usability and learning effectiveness. Design/methodology/approach To assess the usability of IoTTT, Nielsen’s principles were used due to its relevance and popularity for usability assessment. In the usability questionnaire, four attributes were evaluated, notably, learnability, efficiency, errors and satisfaction. As for evaluating learning effectiveness, learning assessment was conducted through pre-tests and post-tests. Two groups of 20 students participated where the first group attended conventional lectures on IoT, whereas the second group used IoTTT for learning same concepts. In the process, data was collected through the usability questionnaire and tests for usability and learning effectiveness assessment. Findings Results revealed a positive score for the usability of the TUI solution with an average rating of 3.9. Although this score demonstrated an acceptable solution, different issues were identified, based on which a set of recommendations have been made in this paper. On the other hand, in the common pre-tests, an average score of 6.40 was obtained as compared to a mean score of 7.33 in the post-tests for all participants. Knowledge gains were significantly higher for students who learnt IoT concepts through the TUI-based system where performance improved by 18 per cent. Originality/value The results revealed in this study are expected to help the research community, course designers and tutors comprehend the prospects of using tangible user interfaces to foster teaching and learning of IoT concepts. In addition, educational solution providers could consider commercialisation prospects of this technology to innovate in teaching and learning, while also building-up on limitations identified within this study

Improving effectiveness of honeypots: predicting targeted destination port numbers during attacks using J48 algorithm

During recent years, there has been an increase in cyber-crime and cybercriminal activities around the world and as countermeasures, effective attack prevention and detection mechanisms are needed. A popular tool to augment existing attack detection mechanisms is the Honeypot. It serves as a decoy for luring attackers, with the purpose to accumulate essential details about the intruder and techniques used to compromise systems. In this endeavor, such tools need to effectively listen and keep track of ports on hosts such as servers and computers within networks. This paper investigates, analyzes and predicts destination port numbers targeted by attackers in order to improve the effectiveness of honeypots. To achieve the purpose of this paper, the J48 decision tree classifier was applied on a database containing information on cyber-attacks. Results revealed insightful information on key destination port numbers targeted by attackers, in addition to how these targeted ports vary within different regions around the world