A Framework for Active Learning: Revisited

Over the past decade, algorithm visualization tools have been researched and developed to be used by Computer Science instructors to ease students’ learning curve for new concepts. However, limitations such as rigid animation frameworks, lack of user interaction with the visualization created, and learning a new language and environment, have severely reduced instructors’ desire to use such a tool. The purpose of this project is to create a tool that overcomes these limitations. Instructors do not have to get familiar with a new framework and learn another language. The API used to create algorithm animation for this project is through Java, a programming language familiar to many instructors. Moreover, not only do the instructors have control over planning the animation, students using the animation will also have the ability to interact with it

Experiences using Z animation tools.

In this paper we describe our experience of using three different animation systems. We searched for and decided to use these tools in the context of a project which involved developing formal versions (in Z) of informal requirements documents, and then showing the formal versions to people in industry who were not Z users (or users of any formal techniques). So, an animator seemed a good way of showing the behaviour of a system described formally without the audience having to learn Z. A requirement, however, that the tools used have to satisfy is that they correctly animated Z (whatever that may mean) and they behave adequately in terms of speed and presentation. We have to report that none of the tools we looked at satisfy these requirements--though to be fair all of them are still under development

Conceptual Information Compression and Efficient Pattern Search

This paper introduces an encoding of knowledge representation statements as regular languages and proposes a two-phase approach to processing of explicitly declared conceptual information. The idea is presented for the simple conceptual graphs where conceptual pattern search is implemented by the so called projection operation. Projection calculations are organised into off-line preprocessing and run-time computations. This enables fast run-time treatment of NP-complete problems, given that the intermediate results of the off-line phase are kept in suitable data structures. The experiments with randomly-generated, middle-size knowledge bases support the claim that the suggested approach radically improves the run-time conceptual pattern search

Towards Python-based Domain-specific Languages for Self-reconfigurable Modular Robotics Research

This paper explores the role of operating system and high-level languages in the development of software and domain-specific languages (DSLs) for self-reconfigurable robotics. We review some of the current trends in self-reconfigurable robotics and describe the development of a software system for ATRON II which utilizes Linux and Python to significantly improve software abstraction and portability while providing some basic features which could prove useful when using Python, either stand-alone or via a DSL, on a self-reconfigurable robot system. These features include transparent socket communication, module identification, easy software transfer and reliable module-to-module communication. The end result is a software platform for modular robots that where appropriate builds on existing work in operating systems, virtual machines, middleware and high-level languages.Comment: Presented at DSLRob 2011 (arXiv:1212.3308

A Pregroup Analysis of Japanese Causatives

PACLIC 21 / Seoul National University, Seoul, Korea / November 1-3, 200

Proceedings of the Second Program Visualization Workshop, 2002

The Program Visualization Workshops aim to bring together researchers who design and construct program visualizations and, above all, educators who use and evaluate visualizations in their teaching. The first workshop took place in July 2000 at Porvoo, Finland. The second workshop was held in cooperation with ACM SIGCSE and took place at HornstrupCentret, Denmark in June 2002, immediately following the ITiCSE 2002 Conference in Aarhus, Denmark

A Comparison of State-Based Modelling Tools for Model Validation

In model-based testing, one of the biggest decisions taken before modelling is the modelling language and the model analysis tool to be used to model the system under investigation. UML, Alloy and Z are examples of popular state-based modelling languages. In the literature, there has been research about the similarities and the differences between modelling languages. However, we believe that, in addition to recognising the expressive power of modelling languages, it is crucial to detect the capabilities and the weaknesses of analysis tools that parse and analyse models written in these languages. In order to explore this area, we have chosen four model analysis tools: USE, Alloy Analyzer, ZLive and ProZ and observed how modelling and validation stages of MBT are handled by these tools for the same system. Through this experiment, we not only concretise the tasks that form the modelling and validation stages of MBT process, but also reveal how efficiently these tasks are carried out in different tools