Mobile Game Based Learning on Online Banking Fraud Security among Young Adults (BFG)

Online banking has brought a huge transformation in many banking and financial services. The percentage of online banking users has increase rapidly across the age including young adults nowadays. In 2011, fraud marks the highest percentage of cyber-crime cases in Malaysia. High concern rises among global expertise on fraud attacks especially in economic and banking sectors. However, the knowledge on this issue among Malaysian young adults is still low. Through the survey conducted, about 63% of young adults do not know about online banking system security. Therefore, this paper presents an alternative approach to educate young adults in Malaysia about online banking fraud. The main deliverables of this study are to identify the online banking fraud security issues that related to young adults, their behavior towards online system security and the development stages. Bypass Fraud Game (BFG) has been proposed based on mobile game-based learning model by mapping with learning theories and approach using Android Eclipse and SDK Development software as the platform. In addition, Technology Acceptance Model (TAM) is used to evaluate the effectiveness on adaption of learning theories in mobile game-based learning. The results show that the game is well received by the users with average response more than 4.00 for TAM elements which are Perceived Usefulness (PU), Perceived Ease of Use (PEOU) and Attitude towards Using (ATU). Through this interactive and fun approach, BFG is expected to help young adults in identifying the methods deployed by cyber criminals as well as precaution action to prevent from being attack

Mobile Game Based Learning on Online Banking Fraud Security among Young Adults (BFG)

Online banking has brought a huge transformation in many banking and financial services. The percentage of online banking users has increase rapidly across the age including young adults nowadays. In 2011, fraud marks the highest percentage of cyber-crime cases in Malaysia. High concern rises among global expertise on fraud attacks especially in economic and banking sectors. However, the knowledge on this issue among Malaysian young adults is still low. Through the survey conducted, about 63% of young adults do not know about online banking system security. Therefore, this paper presents an alternative approach to educate young adults in Malaysia about online banking fraud. The main deliverables of this study are to identify the online banking fraud security issues that related to young adults, their behavior towards online system security and the development stages. Bypass Fraud Game (BFG) has been proposed based on mobile game-based learning model by mapping with learning theories and approach using Android Eclipse and SDK Development software as the platform. In addition, Technology Acceptance Model (TAM) is used to evaluate the effectiveness on adaption of learning theories in mobile game-based learning. The results show that the game is well received by the users with average response more than 4.00 for TAM elements which are Perceived Usefulness (PU), Perceived Ease of Use (PEOU) and Attitude towards Using (ATU). Through this interactive and fun approach, BFG is expected to help young adults in identifying the methods deployed by cyber criminals as well as precaution action to prevent from being attack

10th Annual Student Academic Conference

Minnesota State University Moorhead Student Academic Conference abstract book.https://red.mnstate.edu/sac-book/1009/thumbnail.jp

Machine Learning in Tribology

Tribology has been and continues to be one of the most relevant fields, being present in almost all aspects of our lives. The understanding of tribology provides us with solutions for future technical challenges. At the root of all advances made so far are multitudes of precise experiments and an increasing number of advanced computer simulations across different scales and multiple physical disciplines. Based upon this sound and data-rich foundation, advanced data handling, analysis and learning methods can be developed and employed to expand existing knowledge. Therefore, modern machine learning (ML) or artificial intelligence (AI) methods provide opportunities to explore the complex processes in tribological systems and to classify or quantify their behavior in an efficient or even real-time way. Thus, their potential also goes beyond purely academic aspects into actual industrial applications. To help pave the way, this article collection aimed to present the latest research on ML or AI approaches for solving tribology-related issues generating true added value beyond just buzzwords. In this sense, this Special Issue can support researchers in identifying initial selections and best practice solutions for ML in tribology

New general mechanistic model for predicting civil disturbances and their characteristics

Since the wave of civil violence in the USA in the 1960s, many social theorists have tried to explain why riots occur. Despite at least 50 years of research since then, there is still not enough insight to anticipate large events like the 2011 Arab Spring and London riots. The main goal of this thesis is therefore to improve understanding about how underlying conditions influence and drive riot dynamics, such as the intensity, spread, and duration. I develop a new mechanistic and stochastic agent-based model for riots. Previous models have either only targeted general phenomena associated with riots, or aimed at behaviour specific to a single event. In this thesis I combine both approaches: I demonstrate how the model in which the motivation of the agents is based on general concepts, can be applied to the specific situation of the 2011 London riots. The model reproduces the majority of the behaviour observed in the London riots (r = 0.4-0.8). One of the key factors under investigation is the relationship between protests and outbursts of civil violence. Riots are often preceded by protests, such that a large pool of potential rioters is directly available. I find that the number of times a protest is repeated has greater influence on riot dynamics than the protest crowd size. The support shown during demonstrations might incite false confidence in individuals, potentially leading to quicker escalation. Another question is how contact networks and collective identity influence the spread of violence between different locations. The role of online social media (e.g. Twitter) has been a major focus in trying to explain why the violence in the 2011 Arab spring spread so quickly and so far. I investigate the role of social similarity as another factor that might have contributed to the diffusion of unrest, and demonstrate the existence of a critical transition in riot activity when increasing the density of the contact network in the model. Such increases in density beyond the critical thresholds might have been introduced by online social networks. Finally, I explore the sensitivity to cooperation of different potential riot groups. In some cases, mixed populations with different collective identities can form coalitions within neighbourhoods based on shared grievances, which could lead to increases in riot size and riot probability. I examine the influence of the social structure and spread of these populations over different neighbourhoods, as well as the overlap in grievances and different demographic structures

Using low-cost remote sensing data for geohazard modelling and analysis in Small Island Developing States (examples of Dominica and Cape Verdes).

Geohazards such as flooding and debris flows pose serious threats to livelihood, the physical and built environment especially in mountainous Small Island Developing States (SIDS). Geohazards cause an economic loss of over $2 billion and approximately 300 to 600 deaths and injuries across the Caribbean and Pacific SIDS annually. To mitigate the negative impacts of flooding and debris flow, it is necessary to model and map areas that that susceptible to these hazards and to inform local officials about the potential risk. The study was undertaken on selected localities in two SIDS, specifically Dominica and the Cape Verde. These islands lie on the Atlantic Hurricane Line and are prone to hazards such as flooding and debris flow. A typical example is the 2017 Hurricane Maria that triggered landslides, debris flows and flooding in Dominica, causing substantial loss of life, destruction of properties and economic losses. These islands are SIDS and so they face major challenges in tackling these hazards due to limited financial and human resources coupled with lack of technological advancement. This study utilised low-cost remote sensing data such as drone-derived DEMs and orthophotographs in RAMMS and HEC-RAS to model and map areas vulnerable to debris flow and flooding hazards. Movement of boulders during Hurricane Maria aggravated the level of damages to properties and infrastructures. Therefore, drone-derived orthophotographs were applied in ImageJ to analyse size of boulders moved in relations to the damages and fatalities recorded. Results of the study demonstrated that these methods can be applied in other SIDS to mitigate impact of geohazards such as floods and debris flow