Search for: All records

This article discusses the Preparing Future Leaders and Movers in Engineering and Computer Science (FLAME) program at a Historically Black College and University (HBCU). The FLAME program aims to create a supportive learning community that enhances students' successful completion of their computer science and engineering degrees while fostering their leadership development. The article highlights the program's achievements and challenges and the actions taken to address any shortcomings. It explores FLAME's strategy for cultivating leaders and innovators in engineering and computer science, particularly among low-income students. Additionally, the discussion covers the program's plans to help students identify more strongly as scientists or engineers, increase their completion rates for degrees in computer science, information technology, and engineering, and support their overall success, retention, and graduation. This support includes providing scholarships, mentoring, research opportunities, and professional development resources. 

This article discusses the Preparing Future Leaders and Movers in Engineering and Computer Science (FLAME) program at a Historically Black College and University (HBCU). The FLAME program aims to create a supportive learning community that enhances students' successful completion of their computer science and engineering degrees while fostering their leadership development. The article highlights the program's achievements and challenges and the actions taken to address any shortcomings. It explores FLAME's strategy for cultivating leaders and innovators in engineering and computer science, particularly among low-income students. Additionally, the discussion covers the program's plans to help students identify more strongly as scientists or engineers, increase their completion rates for degrees in computer science, information technology, and engineering, and support their overall success, retention, and graduation. This support includes providing scholarships, mentoring, research opportunities, and professional development resources. 

Large Language Models (LLMs) are increasingly applied across business, education, and cybersecurity domains. However, LLMs can yield varied outputs for the same query due to differences in architecture, training data, and response generation mechanisms. This paper examines model variability and uncertainty by comparing the responses of three LLMs—ChatGPT-4o, Gemini 2.0 Flash, and DeepSeek-V3--to a query on ranking practical intrusion detection systems (IDS). The analysis highlights key similarities and differences in the models’ outputs, offering insight into their respective reasoning and consistency.