Technology-enabled Learning (TEL): YouTube as a Ubiquitous Learning Aid.

The use of social networks such as Facebook, Twitter, and YouTube in the society has become ubiquitous. The advent of communication technologies alongside other unification trends and notions such as media convergence and digital content allow the users of the social network to integrate these networks in their everyday life. There have been several attempts in the literature to investigate and explain the use of social networks such as Facebook and WhatsApp by university students in the Arab region. However, little research has been done on how university students utilise online audiovisual materials in their academic activities in the UAE. This research aims to elucidate the use of YouTube as a learning aid for university students in the UAE. We adopt the technology acceptance model (TAM) as the theoretical framework for this investigation. A quantitative methodology is employed to answer the research question. Primary data consisting of 221 correspondents were analysed, covering patterns of using YouTube as an academic audiovisual learning aid. Statistical techniques including descriptive, correlations, regression tests were used to analyse the data. The study concluded that students use YouTube as a learning tool for their academic studies and enriching their general knowledge; and there is a positive relationship between the use of YouTube videos in academic settings and the students’ overall performance. This study can shed light for teachers, curriculum designers, government entities, and other stakeholders on how to best utilise and integrate the online technology — YouTube — as a learning aid

A Machine Learning Clustering Technique for Autism Screening and Other Applications

Clustering is one of the challenging machine learning techniques due to its unsupervised learning nature. While many clustering algorithms constrain objects to single clusters, K-means overlapping partitioning clustering based methods assign objects to multiple clusters by relaxing the constraints and allowing objects to belong to more than one cluster to better fit hidden structures in the data. However, when datasets contain outliers, they can significantly influence the mean distance of the data objects to their respective clusters, which is a drawback. Therefore, most researchers address this problem by simply removing the outliers. This can be problematic especially in applications such as autism screening, fraud detection, and cybersecurity attacks among others. In this thesis, an alternative solution to this problem is proposed that captures outliers and stores them on the fly within a new cluster, instead of discarding. The new algorithm is named Outlier-based Multi-Cluster Overlapping K-Means Extension (OMCOKE). The algorithm addresses an issue previously ignored by other work in overlapping clustering and therefore benefits various stakeholders as these outliers could have real-life applications. The proposed solution has been evaluated on a crucial behavioural science problem called screening of autistic traits to improve the performance of detecting autism spectrum disorder (ASD) traits and reduce features redundancy. OMCOKE was integrated as a learning algorithm with a semi-supervised ML framework approach called Clustering based Autistic Trait Classification (CATC) in Chapter 5. Based on the experimental results obtained on real datasets related to autism screening OMCOKE was able to identify potential autism cases based on their similarity traits as opposed to conventional scoring functions used by ASD screening tools. Moreover, the empirical results obtained by OMCOKE on different datasets involving children, adolescents, and adults were compared to other results produced by common ML techniques. The results showed that our semi-supervised framework offers models with higher predictive accuracy, sensitivity, and specificity rates than those of other intelligent classification approaches such as Artificial Neural Network (ANN), Random Forest, and Random Trees, and Rule Induction. These models are useful since they are exploited by diagnosticians and other stakeholders involved in ASD screening besides highlighting the most influential features. The chapters in this thesis have been disseminated or are under review in various reputable journals and in refereed conference proceedings

Women in Political Positions and Countries' Level of Happiness

This study discusses the relationship between a country’s happiness, and relevant country characteristics including gender inequality, levels of corruption, and the percentage of women in parliamentary positions. The aim of the study is to understand how these variables change according to female representation in political leadership. Secondary source data was collected and correlation analyses were performed between the variables using the SPSS statistical program. Results show that the lower the Gender Inequality Index, the higher the percentage of Women in Parliament Positions and the higher the Happiness Index. Furthermore, the results indicate that a higher number of women in leadership position is associated with a lower corruption level and a higher degree of Happiness. Results suggest that the context in which women reach political positions is characterized by less corruption and gender inequality alongside greater happiness in the country

Avoiding the Phishing Bait: The Need for Conventional Countermeasures for Mobile Users

According to the international Anti-Phishing Work Group (APWG), phishing activities have significantly risen over the last few years, and users are becoming more susceptible to online and mobile fraud. Machine Learning (ML) techniques have the potential for building technical anti-phishing models, a majority of them have yet to be applied in a real-time environment. ML models also require domain experts to interpret the results. This gives conventional techniques a vital role as supportive tools for a wider audience, especially novice users, in order to reduce the rate of phishing attacks. Our paper aims at raising awareness and educating users on phishing in general and mobile phishing in particular from a conventional perspective, unlike existing reviews that are based on data mining and machine learning. This will equip individuals with knowledge and skills that may prevent phishing on a wider context within the mobile users’ community

Implementación de un prototipo funcional de aprendizaje de máquina para identificar correos electrónicos de Spear Phishing

Trabajo de investigaciónEste trabajo tiene como propósito la detección de correos electrónicos Spear Phishing a mediante un prototipo web, debido a que las técnicas de ingeniería social son muy usadas hoy en día para robar a los usuarios datos de identidad personal y/o credenciales de sus cuentas financieras, por esta razón, todas las personas deben implementar una medida para detectar estos ataques de ingeniería social.2 JUSTIFICACIÓN 3 PLANTEAMIENTO DEL PROBLEMA 4 OBJETIVOS 5 MARCOS DE REFERENCIA 6 ESTADO DEL ARTE 7 METODOLOGÍA 8 DESARROLLO DE LA PROPUESTA 9 INSTALACIÓN Y EQUIPO REQUERIDO 10 RESULTADOS 11 CONCLUSIONES 12 TRABAJOS FUTUROS 13 BIBLIOGRAFÍA 14 ANEXOSPregradoIngeniero de Sistema

Back to Basics: Where Is Allah? - A Survey of Generation Z Youth at the Canadian University of Dubai

The belief of a heavenly God is enshrined to all Abrahamic religions which form the three major religions of the world today. Muslims believe in Allah who is above the seven heavens. The youth in the United Arab Emirates (UAE) study Islamic courses as part of their high school curriculum and are required to take at least one Islamic course at the university level to gain credit hours towards their general education (GENED). This paper provides an insight of what the youth studying in the UAE think of where Allah is. Our analysis shows that a big number of Muslim youth were not sure, especially those from the Middle Eastern and Arab countries bringing to conclusion that this subject needs to be revisited again in the course work