Recent advances in artificial intelligence (AI), including the widespread adoption of foundational models, have triggered changes across Computing Science (CS) programs. Developments include, but are not limited to, revisions to assessment practices, updates to academic integrity policies, curriculum redesign to integrate emerging concepts, and the growing use of conversational agents to support instruction. These developments aim to address effective preparation of graduates for an evolving AI innovation landscape.

Computational thinking (CT) integration in elementary mathematics engages young learners in the decomposition of complex problems and the construction of iterative approaches to mathematical thinking. To effectively integrate MATH+CT, we need to build teachers' capacity in developing knowledge of CT concepts and creating Math+CT activities. This can positively influence students' mathematical outcomes and their readiness for computer science (CS) in middle and high school. Although various professional development programs aim to build teachers' CT knowledge, limited research exists on how teachers apply this knowledge to classroom-based Math+CT activities. Simultaneously, the rapid improvement of large Language Models (LLMs) creates a catalyst for building an innovative resource space to support elementary teachers' integration of MATH+CT in their existing school or district curriculum. In this poster, we present a tool that leverages LLMs to support teachers in creating Scratch programs designed to explore and deepen students' understanding of mathematical concepts.

In recent years, there has been a growing emphasis on using computational thinking (CT) to teach mathematical concepts. The integration of math+CT enables students to gain a deeper understanding of both mathematical and computer science (CS) concepts. Although numerous professional development programs provide teachers with a solid understanding of CT and methods for integrating it into the curriculum, there has been little research on how teachers apply this knowledge to the design and development of math+CT activities. Moreover, the majority of integrated activities are concentrated in the upper elementary grades, while the early grades (i.e., K-2) remain underexplored. The purpose of this study is to describe how teachers translate their CT knowledge in the collaborative design of an integrated math+CT activity focused on number sense and mathematical operations for grades K-2. Research faculty from the CS and Teacher Education departments designed block-based programming activities for K-2 students to explore and make sense of place value representations of the numbers 1 - 1000. In-service elementary teachers in an online graduate STEM CT course (N = 13) experienced these activities as learners and provided feedback for redesign from their perspectives as classroom teachers. Qualitative analysis of collaborative problem-solving slides and individual reflections revealed how teachers connected their mathematical knowledge for teaching place value to their emergent understanding of CT concepts of abstraction, decomposition, and debugging. This research bridges the gap between enthusiasm for math+CT integration and teacher capacity to use CT to promote creative mathematical thinking and problem-solving in K-2 classrooms.

Virtual machine (VM) replication is a critical task in any cloud computing platform to ensure the availability of the cloud service for the end user. In this task, one primary VM resides on a physical machine (PM) and one or more replicas reside on separate PMs. In cloud computing, VM placement (VMP) is a well‐studied problem in terms of different goals, such as power consumption reduction. The VMP problem can be solved by using heuristics, namely, first‐fit and meta‐heuristics such as the genetic algorithm. Despite extensive research into the VMP problem, there are few works that consider VM replication when choosing a VMP. In this context, we proposed studying the problem of optimal VMP considering VM replication requirements. The proposed work frames the problem at hand as a multiobjective problem and adapts a nondominated sorting genetic algorithm (NSGA‐III) to address the problem. VM replicas’ placement should consider several dimensions such as the geographical distance between the PM hosting the primary VM and the other PMs hosting the replicas. In addition, to this end, the proposed model aims to minimize (1) power consumption, (2) performance degradation, and (3) the distance between the PMs hosting the primary VM and its replica(s). The proposed method is thoroughly tested on a variety of computing environments with various heterogeneous VMs and PMs, including compute‐intensive and memory‐intensive environments. The obtained results illustrate the performance disparity between the adapted NSGA‐III and MOEA/D methods and other methods of comparison, including heuristic and meta‐heuristic approaches, with NSGA‐III outperforming other comparison methods. For instance, in memory‐intensive and in heterogeneous environments, the NSGA‐III method’s performance was superior to the first‐fit, next‐fit, best‐fit, PSO, and MOEA/D methods by 58%, 62%, 64%, 55%, and 31%, respectively.

Abstract In the study of complex physical systems, scientists use simulations to study the effects of different models and parameters. Seeking to understand the influence and relationships among multiple dimensions, they typically run many simulations and vary the initial conditions in what are known as ‘ensembles’. Ensembles are then a number of runs that are each multi‐dimensional and multi‐variate. In order to understand the connections between simulation parameters and patterns in the output data, we have been developing an approach to the visual analysis of scientific data that merges human expertise and intuition with machine learning and statistics. Our approach is manifested in a new visualization tool, GLEE (Graphically‐Linked Ensemble Explorer), that allows scientists to explore, search, filter and make sense of their ensembles. GLEE uses visualization and semantic interaction (SI) techniques to enable scientists to find similarities and differences between runs, find correlation(s) between different parameters and explore relations and correlations across and between different runs and parameters. Our approach supports scientists in selecting interesting subsets of runs in order to investigate and summarize the factors and statistics that show variations and consistencies across different runs. In this paper, we evaluate our tool with experts to understand its strengths and weaknesses for optimization and inverse problems.

A system's memory size is often dictated by worst-case workloads with highest memory requirements; this causes memory to be underutilized in the common case when the system is not running its worst-case workloads. Cognizant of this memory underutilization problem, many prior works have studied memory utilization and explored how to improve it in the context of cloud.

Nowadays, users rely on cloud storage as it offers cheap and unlimited data storage that is available for use by multiple devices (e.g. smart phones, notebooks, etc.). Although these cloud storage services offer attractive features, many customers are not adopting them, since data stored in these services is under the control of service providers and this makes it more susceptible to security risks. Therefore, in this paper, we addressed the problem of ensuring data confidentiality against cloud and against accesses beyond authorized rights by designing a secure cloud storage system framework that simultaneously achieves data confidentiality and fine-grained access control on encrypted data. This framework is built on a trusted third party (TTP) service that can be employed either locally on users' machine or premises, or remotely on top of cloud storage services for ensuring data confidentiality. Furthermore, this service combines multi-authority ciphertext policy attribute-based encryption (MA-CP-ABE) and attribute-based Signature (ABS) for achieving many-read-many-write fine-grained data access control on storage services. Last but not least, we validate the effectiveness of our design by carrying out a security analysis.