2019 Oklahoma Research Day Full Program

Oklahoma Research Day 2019 - SWOSU Celebrating 20 years of Undergraduate Research Successes

Communities in (Digital) Space: Creating Networks for Daily Living Through Pervasive Media

Studies of online communities often focus either on communities that produce texts or the texts with which individuals engage. This dissertation examines online communities that practice in ongoing activities, in their leisure time, often with no end goal of producing any final text. Through interviews, surveys, and community forum analysis of running, gaming, and translation communities, this study finds that place and everyday habits factor heavily into the ways that sustained online communities structure their work. “Place” can have several meanings within this context, including the communities valuing specific locations or working with specific individuals because of where they live. Due to the rise in use of pervasive mobile devices, online community access often weaves into members’ offline lives. This knowledge of life ancillary to online community adds a layer of affective work to online community participation. Throughout the data collected from these communities, stories pertaining to the work of community maintenance dominated the conversation. Participants defined “work” as managing community involvement around other obligations, maintaining relationships across distances, and acknowledging the benefits that corporate entities derive from these communities. By investigating work within this context, we expand our understanding of the ways less visible populations work online in their leisure time

Adaptive Cybersecurity Training Framework for Social Media Risks

Social media has become embedded in our everyday lives, personal activities, and the workplace. Thus, educating users on emerging cybersecurity challenges for social media has become imperative. In this project, a systematic literature review (SLR) was conducted and a mix of approach analyses to derive a framework that identifies the activities involved in adapting cybersecurity training for social media risks. I collected answers from 641 Kuwaiti employees in various sectors: education, healthcare, leadership and management, arts, entertainment, the police, and military, and interviewed 25 people who serve as policymakers, cybersecurity trainers, and those who have experienced cybersecurity training before. The study found that a one-fits-all training approach is highly ineffective, as people’s understanding and knowledge can vary greatly. Features such as gender, age, educational level, job roles, and the trainees’ training preferences and perceptions are essential considerations for developing a robust training system. Additionally, the study found that job role and age constitute the main factors associated with social media cybersecurity risks. The findings reveal that employees working in the business and financial sectors are the riskiest group, as far as cybersecurity is concerned. Female employees are more vulnerable to cyberattacks than male employees, and the youngest employees are the most risk prone, employees with less than two years of experience, and those who are 55 years old or more, need more cybersecurity training, due to their lack of awareness on the subject. This work has led to formulate a risk equation that can assist policymakers and training providers in defining countermeasures against risks and prioritize the training for those who need it the most. The framework and its process were validated through several strategies involving 38 case studies, surveys, and interviews. The novel contribution of this research is the proposal of the framework, which is a high-level, holistic framework that can support and promote organizations in mitigating social media risks

Implementacija umjetne inteligencije i njezin budući potencijal

Firstly, in the paper, I explored the history of artificial intelligence (AI) thought spanning from the early conceptual beginnings, then through early examples of primitive AI applications and all the way to recent feats in this field. Next, I analyzed types of AI, both present and future, encompassing two wide schools of thought; after which I detailed the pathways to achieving practical implementation of AI through machine learning (ML) and deep learning (DL) as well as a brief history of TensorFlow. The following chapters focused on analyzing case studies of AI application in the fields of banking and finance from the financial sector, and transportation in general, with the ensuing critical analyses. The final chapter is concerned with future implementation of AI