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This paper presents an innovative testing framework, testFAILS, designed for the rigorous evaluation of AI Linguistic Systems, with a particular emphasis on various iterations of ChatGPT. Leveraging orthogonal array coverage, this framework provides a robust mechanism for assessing AI systems, addressing the critical question, "How should we evaluate AI?" While the Turing test has traditionally been the benchmark for AI evaluation, we argue that current publicly available chatbots, despite their rapid advancements, have yet to meet this standard. However, the pace of progress suggests that achieving Turing test-level performance may be imminent. In the interim, the need for effective AI evaluation and testing methodologies remains paramount. Our research, which is ongoing, has already validated several versions of ChatGPT, and we are currently conducting comprehensive testing on the latest models, including ChatGPT-4, Bard and Bing Bot, and the LLaMA model. The testFAILS framework is designed to be adaptable, ready to evaluate new bot versions as they are released. Additionally, we have tested available chatbot APIs and developed our own application, AIDoctor, utilizing the ChatGPT-4 model and Microsoft Azure AI technologies. 

This paper presents an innovative approach, applicable to all research-based fields, that identifies and broadly engages future computer science researchers. The Computing Alliance of Hispanic Serving Institutions (CAHSI) piloted a national virtual Research Experience for Undergraduates (vREU) during the summer of 2020. Funded by an NSF grant, the goal of the program was to ensure that students, in particular those with financial need, had opportunities to engage in research and gain critical skills while advancing their knowledge and financial resources to complete their undergraduate degrees and possibly move to advanced studies. The vREU pilot provided undergraduate research experiences for 51 students and 21 faculty drawn from 14 colleges and universities. The Affinity Research Group (ARG) model, based on a cooperative learning model, was used to guide faculty mentors throughout the eight-week vREU. ARG is a CAHSI signature practice with a focus on deliberate, structured faculty and student research, technical, communication, and professional skills development. At weekly meetings, faculty were provided resources and discussed a specific skill to support students’ research experience and development, which faculty put into immediate practice with their students. Evaluation findings include no statistical difference in student development between the face-to-face and virtual models with faculty and the benefit of training as an opportunity for faculty professional growth and impact. This faculty development model allows for rapid dissemination of the ARG model through practice and application with weekly faculty cohort meetings, coaching, and reflection. 

Abstract—Virtual Network Functions (VNFs) are software implementation of middleboxes (MBs) (e.g., firewalls and proxy servers) that provide performance and security guarantees for virtual machine (VM) cloud applications. In this paper, we study a new VM flow migration problem for dynamic VNF-enabled cloud data centers (VDCs). The goal is to migrate the VM flows in the dynamic VDCs to minimize the total network traffic while load-balancing VNFs with limited processing capabilities. We refer to the problem as FMDV: flow migration in dynamic VDCs. We propose an optimal and efficient minimum cost flow-based flow migration algorithm and two benefit-based efficient heuristic algorithms to solve the FMDV. Via extensive simulations, we show that our algorithms are effective in mitigating dynamic cloud traffic while achieving load balance among VNFs. In particular, all our algorithms reduce dynamic network traffic in all cases and our optimal algorithm always achieves the best traffic-mitigation effect, reducing the network traffic by up to 28% compared to the case without flow migration.