Alice (programming language)

Abstract: We describe Storytelling Alice, a programming environment that introduces middle school girls to computer programming as a means to the end of creating 3D animated stories. Storytelling Alice supports story creation by providing 1) a set of high-level animations, that support the use of social characters who can interact with one another, 2) a collection of 3D characters and scenery designed to spark story ideas, and 3) a tutorial that introduces users to writing Alice programs using story-based examples. In a study comparing girls' experiences learning to program using Storytelling Alice and a version of Alice without storytelling support (Generic Alice), we found that users of Storytelling Alice and Generic Alice were equally successful at learning basic programming constructs. Participants found Storytelling Alice and Generic Alice equally easy to use and entertaining. Users of Storytelling Alice were more motivated to program; they spent 42% more time programming, were more than 3 times as likely to sneak extra time to work on their programs, and expressed stronger interest in future use of Alice than users of Generic Alice.

Abstract: We present lessons learned from developing Alice, a 3D graphics programming environment designed for undergraduates with no 3D graphics or programming experience. Alice is a Windows 95/NT tool for describing the time-based and interactive behavior of 3D objects, not a CAD tool for creating object geometry. Our observations and conclusions come from formal and informal observations of hundreds of users. Primary results include the use of LOGO-style egocentric coordinate systems, the use of arbitrary objects as lightweight coordinate systems, the launching of implicit threads of execution, extensive function overloading for a small set of commands, the careful choice of command names, and the ubiquitous use of animation and undo.

Abstract: Malicious attack detection is one of the critical cyber-security challenges in the peer-to-peer smart grid platforms due to the fact that attackers’ behaviours change continuously over time. In this paper, we evaluate twelve Machine Learning (ML) algorithms in terms of their ability to detect anomalous behaviours over the networking practice. The evaluation is performed on three publicly available datasets: CICIDS-2017, UNSW-NB15 and the Industrial Control System (ICS) cyber-attack datasets. The experimental work is performed through the ALICE high-performance computing facility at the University of Leicester. Based on these experiments, a comprehensive analysis of the ML algorithms is presented. The evaluation results verify that the Random Forest (RF) algorithm achieves the best performance in terms of accuracy, precision, Recall, F1-Score and Receiver Operating Characteristic (ROC) curves on all these datasets. It is worth pointing out that other algorithms perform closely to RF and that the decision regarding which ML algorithm to select depends on the data produced by the application system.

Abstract: In this paper, we describe a pedagogy for an undergraduate programming course using Alice 3 and Java. We applied the educational theory of mediated transfer to develop a new version of the Alice system and accompanying instructional materials. The pedagogy was implemented and tested over two years. Student test scores in experimental, treatment course sections showed dramatic improvement over scores in comparable non-treatment sections.