This innovative practice paper presents a work-inprogress on the design of a 'play-kit' to introduce incoming first-year university students to diverse thinking styles through playful interactions, addressing the need for adaptable problemsolving skills development required to tackle increasingly complex global socio-technical challenges. Our initial design stage involves creating a prototype physical workbook to stimulate computational thinking skills through play. We will adapt lessons from existing computational thinking material, originally designed as a classroom-based tool for primary school students. We customize lessons for university students, and re-work them so that they become self-directed learning activities. Our workbook emphasizes essential computational components - decomposition, algorithms, pattern recognition, logic, representation, and abstraction. In time, the project will offer both physical and online 'Learning to Think' play-kits to widen accessibility and suit a diversity of learning styles.

Generative Artificial Intelligence (AI) and Large Language Models (LLMs) such as ChatGPT are revolutionizing the landscape of learning and teaching. They excel in understanding and creating natural language texts, thereby captivating students with their quick and well-crafted responses. While some perceive AI simply as a tool to reduce workload, our study appreciates these technologies for their ability to beautifully augment human capabilities. In this study, we tasked ChatGPT with designing a relational database for an online food delivery system, similar to an early university computer science assignment. This paper explains the attention mechanism, which is a crucial component in LLMs, enabling them to focus on specific parts of the presented input (prompt) and enhances their ability to ‘understand’ context. Through a series of iterative prompt refinements, we evaluate ChatGPT's effectiveness in developing this database, with a goal to enhance the accuracy and relevance of its responses. Our findings reveal both the benefits and limitations of using LLMs in education, highlighting their potential to significantly enrich the learning experience.

This paper explores the complexities of implementing a student-centered approach within a software engineering conversion degree. It addresses the challenges and opportunities presented by students with diverse backgrounds and expectations, as well as varying levels of prior experience. Through a comprehensive study, surprising levels of previous experience among students were revealed, despite their non-computing undergraduate degrees. However, this diversity in experience is accompanied by differing degrees of confidence in their programming knowledge. The paper underscores the necessity to shift from traditional, uniform educational methods to a personalized, student-centered model that accommodates individual expectations and the ever-evolving demands of the software industry. In today's landscape of software engineering education, mere memorization of programming syntax and facts falls short. It is imperative that students become active problem solvers, capable of applying fundamental principles in real-world contexts. The principles of learner-centered education, including individualized learning, active and problem-based learning, collaborative learning, and continuous feedback, are discussed as vital components of a student-centered approach. The research methodology involved surveying students to understand their prior experiences, and career aspirations within the context of student-centered education. Results demonstrated that many students had prior experience, with a substantial percentage having completed short online courses in programming. However, the majority expressed neutrality or a lack of confidence in their software engineering knowledge. On the other hand, most students were confident in their ability to succeed in the course. In this paper, we advocate a shift towards personalized, student-centered education in software engineering, highlighting the importance of understanding students' needs and expectations to create more effective and engaging learning experiences.

Safe high-speed autonomous navigation for MAVs in unknown environments requires fast planning to enable the robot to adapt and react quickly to incoming information about obstacles within the world. Furthermore, when operating in environments not known a priori, the robot may make decisions that lead to dead ends, necessitating global replanning through a map of the environment outside of a local planning grid. This work proposes a computationally-efficient planning architecture for safe high-speed operation in unknown environments that incorporates a notion of longer-term memory into the planner enabling the robot to accurately plan to locations no longer contained within a local map. A motion primitive-based local receding horizon planner that uses a probabilistic collision avoidance methodology enables the robot to generate safe plans at fast replan rates. To provide global guidance, a memory-efficient sparse topological graph is created online from a time history of the robot's path and a geometric notion of visibility within the environment to search for alternate pathways towards the desired goal if a dead end is encountered. The safety and performance of the proposed planning system is evaluated at speeds up to 10m/s, and the approach is tested in a set of large-scale, complex simulation environments containing dead ends. These scenarios lead to failure cases for competing methods; however, the proposed approach enables the robot to safely reroute and reach the desired goal.

In this work, we consider the problem of online centralized kinodynamic multi-robot replanning (from potentially non-stationary initial states) and coordination in known and cluttered workspaces. Offline state lattice reachability analysis is leveraged to decouple the planning problem into two sequential graph searches-one in the explicit geometric graph of the environment and the other in the graph of the higher-order derivatives of the robot's state-in a manner such that the intermediate vertices of a safe set of geometric paths are guaranteed to have a feasible assignment of higher-order derivatives. Without additional iterative refinement procedures, the resulting time parameterized polynomial trajectories are dynamically feasible and collision-free. Planning results with up to 20 robots in two and three dimensional workspaces suggest the suitability of the proposed approach for multi-robot replanning in known environments.

Summary The Pacific Rim Application and Grid Middleware Assembly (PRAGMA) is an international community of researchers that actively collaborate to address problems and challenges of common interest in eScience. The PRAGMA Experimental Network Testbed (PRAGMA‐ENT) was established with the goal of constructing an international software‐defined network (SDN) testbed to offer the necessary networking support to the PRAGMA cyberinfrastructure. PRAGMA‐ENT is isolated, and PRAGMA researchers have complete freedom to access network resources to develop, experiment, and evaluate new ideas without the concerns of interfering with production networks. In the first phase, PRAGMA‐ENT focused on establishing an international L2 backbone. With support from the Florida Lambda Rail, Internet2, PacificWave, Japan Gigabit Network, and TaiWan Advanced Research and Education Network, PRAGMA‐ENT backbone connects openflow‐enabled switches at University of Florida, University of California, San Diego, Nara Institute of Science and Technology (Japan), Osaka University (Japan), National Institute of Advanced Industrial Science and Technology (Japan), and National Applied Research Laboratories (Taiwan). The second phase of PRAGMA‐ENT consisted of an evaluation of technologies for the control plane that enables multiple experiments (ie, OpenFlow controllers) to coexist. Preliminary experiments with FlowVisor revealed some limitations leading to the development of a new approach, called AutoVFlow. This paper describes our experience in the establishment of PRAGMA‐ENT backbone (with international L2 links), its current status, and plans for the control plane. Discussion of preliminary application ideas, including optimization of routing control; multipath routing control; extending the backbone using overlay network; and remote visualization are also discussed.

A core mission of the Integrated Digitized Biocollections (iDigBio) project is the building and deployment of a cloud computing environment customized to support the digitization workflow and integration of data from all U.S. non-federal biocollections. iDigBio chose to use cloud computing technologies to deliver a cyber infrastructure that is flexible, agile, resilient, and scalable to meet the needs of the biodiversity community. In this context, this paper describes the integration of open source cloud middleware, applications, and third party services using standard formats, protocols, and services. In addition, this paper demonstrates the value of the digitized information from collections in a broader scenario involving multiple disciplines.

This paper presents a new on-chip time measurement architecture which is based on the time-to-digital conversion (TDC) method that is capable of achieving a timing resolution of tens of femtoseconds without the use of external automatic test equipment (ATE). This is the highest temporal resolution that has been reported to-date and is achieved by the use of the homodyne technique. The proposed architecture has been designed using a 0.12mum CMOS process and simulation results based on foundry transistor models indicates that it is possible to achieve a timing resolution of 40 fs. The time measurement architecture is standalone and occupies a small silicon area, 150mum by 180mum, making it attractive for high resolution on-chip time measurement

This paper describes a programmable time measurement architecture that facilitates memory characterization. We have created a standalone time measurement architecture that can measure rise time, fall time, pulse width and propagation delay time measurements without the need of additional circuitry as presented in M. J. Hsiaoet al. (2004) or circuit duplication based in T. Xia and J. C. Lo (2003). This is achieved by the use of time-to-digital conversion (TDC) based on the dual-slope principle. The key feature of the proposed architecture is programmability through the use of a novel programmable input stage. Furthermore, a current steering time-to-voltage converter (TVC) is used in order to improve the linearity and dynamic range as compared to recent designs. The proposed architecture has been designed using 0.18/spl mu/m CMOS process and results from simulations using foundry models suggest it is possible to achieve a timing resolution of 103ps. The measurement core size is 110/spl mu/m /spl times/ 75 /spl mu/m.