Machine Learning (ML) analyzes, and processes data and discover patterns. In cybersecurity, it effectively analyzes big data from existing cybersecurity attacks and develop proactive strategies to detect current and future cybersecurity attacks. Both ML and cybersecurity are important subjects in computing curriculum, but using ML for cybersecurity is not commonly explored. This paper designs and presents a case study-based portable labware experience built on Google's CoLaboratory (CoLab) for a ML cybersecurity application to provide students with hands-on labs accessing from anywhere and anytime, reducing or eliminating tedious installations and configurations. This approach allows students to focus on learning essential concepts and gaining valuable experience through hands-on problem solving skills. Our preliminary results and student evaluations are reported for a case-based hands-on regression labware in cyber fraud prediction using credit card fraud as an example.
Abstract The purpose of this presentation is to share our ongoing efforts in support for S-STEM scholars and to highlight the lessons learned. The NSF S-STEM program is designed to empower academically gifted, low-income students to pursue successful careers in promising STEM fields. In August 2022, the Computer Science (CS) department at Tuskegee University (TU) was awarded a S-STEM grant, and we successfully recruited and retained the first cohort of six talented, low-income, first-year African American students in 2023. Over the past year, we have provided a series of mentoring and professional development opportunities to the S-STEM scholars to support their personal and professional growth and foster their leadership skills. Several of these opportunities were extended to the entire TU community to maximize the program's impact. Our efforts include the following. First, we established a mentoring program in which successful senior students, who recently secured positions in major technology companies and government agencies, or entered graduate schools, met with the S-STEM scholars. They shared their life stories and provided tips on college life and career development. Second, to help first-year CS students overcome coding barriers, we offered an introductory coding seminar series called "Begin to Code" (B2C). B2C aimed to boost confidence and persistence among scholars and other CS students by incorporating Apple's Sphero Bolt and iPad. Three first-year S-STEM scholars were actively involved in designing and teaching the seminars, which provides them with professional and leadership development opportunities. Additionally, the B2C seminar contributed to the recruitment of the second cohort of S-STEM scholars. First-year students who attended the seminar had the opportunity to interact with previous S-STEM scholars and the project leader, gaining a better understanding of the program's benefits and requirements. Third, to bridge the gap between the computer science curriculum of TU and the skills needed for learning AI and data science, we conducted a series of Python coding seminars open to the entire TU community. These seminars were taught by two CS juniors with internship experience at big tech companies, fostering strong connections between S-STEM scholars and junior CS students while also exposing other students to Python coding. Fourth, to nurture the leadership skills of our scholars, we provided them with opportunities, such as mentoring high school students attending a summer academy supported by another NSF project. Additionally, these scholars hosted booth activities on TU STEM day to introduce local high school students to CS in a fun and engaging manner. The program evaluation of these initiatives demonstrated valuable impact. For example, a total of 13 first-year students participated in the first B2C seminar and continued to engage in activities every two weeks. Eleven students, including five S-STEM scholars, attended the first Python seminar. Some students whose majors were not CS also benefited from these efforts. However, we also encountered several challenges. The number of participants in each seminar decreased over time due to competing school events and other course commitments. We continue to explore ways to adjust the program to better support each of the S-STEM scholars.
This thesis is submitted in partial fulfilment for the degree Master of Architecture [Professional] at the University of the Witwatersrand, Johannesburg, South Africa, in the year 2015
This bachelor thesis researches the question of “How do Swedish interior design companies tailor their e-commerce marketing strategies to cope with the phenomenon of advertising blindness?” through ...
Machine Learning (ML) analyzes, and processes data and develop patterns. In the case of cybersecurity, it helps to better analyze previous cyber attacks and develop proactive strategy to detect and prevent the security threats. Both ML and cybersecurity are important subjects in computing curriculum, but ML for cybersecurity is not well presented there. We design and develop case-study based portable labware on Google CoLab for ML to cybersecurity so that students can access and practice these hands-on labs anywhere and anytime without time tedious installation and configuration which will help students more focus on learning of concepts and getting more experience for hands-on problem solving skills.
This case illustrates how the Gordon Growth Model is employed to estimate the value of a firm’s stock. The model determines the value of stock based on dividends, growth rate, and the cost of capital. The Capital Asset Pricing Model (CAPM) is employed to calculate the cost of capital. Both economic analysis and ratio analysis are used to examine the impact of external and internal factors on share worth. The case discusses why the market share price may vary from an estimation of its worth. This case study can be used in an Introduction to Investments course, an Advanced Investments course, or a first level MBA graduate course.
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