The Spartan-3E Tutorial 1 : Introduction to FPGA Programming, Version 2.0

This tutorial is designed to help new users become familiar with using the Spartan-3E board. The tutorial steps through: writing a small program in VHDL which carries out simple combinational logic; connecting the program inputs and outputs to the switches, buttons and LEDs on the Spartan-3E board; downloading the program to the Spartan-3E board using version 14.7 of the Xilinx ISE; and simulating the program using the iSim Simulator.

Towards a spatial language for mobile robots

We present a framework and first set of simulations for evolving a language for communicating about space. The framework comprises two components: (1) An established mobile robot platform, RatSLAM, which has a "brain" architecture based on rodent hippocampus with the ability to integrate visual and odometric cues to create internal maps of its environment. (2) A language learning system based on a neural network architecture that has been designed and implemented with the ability to evolve generalizable languages which can be learned by naive learners. A study using visual scenes and internal maps streamed from the simulated world of the robots to evolve languages is presented. This study investigated the structure of the evolved languages showing that with these inputs, expressive languages can effectively categorize the world. Ongoing studies are extending these investigations to evolve languages that use the full power of the robots representations in populations of agents.

Language change in socially structured populations

Language contact is a significant external social factor that impacts on the change in natural languages over time. In some circumstances this corresponds to language competition, in which individuals in a population choose one language over another based on their social interactions. We investigated the dynamics of language change in two initially separate populations of agents that were then mixed with levels of influence determined by the social classes of the two populations, with 16 different combinations tested. As expected, the study found that how the communities interact with each other impacts on the communal language developed. However, it was also found that the acquisition of new words was substantial even with limited interaction between populations and low levels of influence, and that comprehension could be well established across language groups even when production of words from the other language group was low.

Affordances of ICTs: an environmental study of a French language unit offered at university level

This project investigates the integration of Information Communication Technologies (ICTs) into educational settings by closely looking at the uptake of the perceived affordances offered by ICTs by students enrolled in a French language course at Queensland University of Technology. This cross-disciplinary research uses the theoretical concepts of: Ecological Psychology (Gibson, 1979; Good, 2007; Reed, 1996); Ecological Linguistics (Greeno, 1994; Leather & van Dam, 2003; van Lier 2000, 2003, 2004a, 2004b); Design (Norman, 1988, 1999); Software Design/ Human-Computer Interaction (Hartson, 2003; McGrenere & Ho, 2000); Learning Design (Conole & Dyke, 2004a, 2004b; Laurillard et al. 2000;); Education (Kirschner, 2002; Salomon, 1993; Wijekumar et al., 2006) and Educational Psychology (Greeno, 1994). In order to investigate this subject, the following research questions, rooted in the theoretical foundations of the thesis, were formulated: (1) What are the learners’ attitudes towards the ICT tools used in the project?; (2) What are the affordances offered by ICTs used in a specific French language course at university level from the perspective of the teacher and from the perspective of language learners?; (3) What affordances offered by ICT tools used by the teacher within the specific teaching and learning environment have been taken up by learners?; and (4) What factors influence the uptake by learners of the affordances created by ICT tools used by the teacher within the specific teaching and learning environment? The teaching phase of this project, conducted between 2006 and 2008, used Action Research procedures (Hopkins, 2002; McNiff & Whitehead, 2002; van Lier 1994) as a research framework. The data were collected using the following combination of qualitative and quantitative methods: (1) questionnaires administered to students (Hopkins, 2002; McNiff & Whitehead, 2002) using Likert-scale questions, open questions, yes/no questions; (2) partnership classroom observations of research participants conducted by Research Participant Advocates (Hopkins, 2002; McNiff & Whitehead, 2002); and (3) a focus group with volunteering students who participated in the unit (semi-structured interview) (Hopkins, 2002; McNiff & Whitehead, 2002). The data analysis confirms the importance of a careful examination of the teaching and learning environment and reveals differences in the ways in which the opportunities for an action offered by the ICTs were perceived by teacher and students, which impacted on the uptake of affordances. The author applied the model of affordance, as described by Good (2007), to explain these differences and to investigate their consequences. In conclusion, the teacher-researcher considers that the discrepancies in perceiving the affordances result from the disparities between the frames of reference and the functional contexts of the teacher-researcher and students. Based on the results of the data analysis, a series of recommendations is formulated supporting calls for careful analysis of frames of reference and the functional contexts of all participants in the learning and teaching process. The author also suggests a modified model of affordance, outlining the important characteristics of its constituents.

Medical semantic similarity with a neural language model

Advances in neural network language models have demonstrated that these models can effectively learn representations of words meaning. In this paper, we explore a variation of neural language models that can learn on concepts taken from structured ontologies and extracted from free-text, rather than directly from terms in free-text. This model is employed for the task of measuring semantic similarity between medical concepts, a task that is central to a number of techniques in medical informatics and information retrieval. The model is built with two medical corpora (journal abstracts and patient records) and empirically validated on two ground-truth datasets of human-judged concept pairs assessed by medical professionals. Empirically, our approach correlates closely with expert human assessors ($\approx$ 0.9) and outperforms a number of state-of-the-art benchmarks for medical semantic similarity. The demonstrated superiority of this model for providing an effective semantic similarity measure is promising in that this may translate into effectiveness gains for techniques in medical information retrieval and medical informatics (e.g., query expansion and literature-based discovery).

BP-XACML: An authorisation policy language for business processes

XACML has become the defacto standard for enterprise- wide, policy-based access control. It is a structured, extensible language that can express and enforce complex access control policies. There have been several efforts to extend XACML to support specific authorisation models, such as the OASIS RBAC profile to support Role Based Access Control. A number of proposals for authorisation models that support business processes and workflow systems have also appeared in the literature. However, there is no published work describing an extension to allow XACML to be used as a policy language with these models. This paper analyses the specific requirements of a policy language to express and enforce business process authorisation policies. It then introduces BP-XACML, a new profile that extends the RBAC profile for XACML so it can support business process authorisation policies. In particular, BP-XACML supports the notion of tasks, and constraints at the level of a task instance, which are important requirements in enforcing business process authorisation policies.

Analysing student programs in the PHP intelligent tutoring system

Programming is a subject that many beginning students find difficult. The PHP Intelligent Tutoring System (PHP ITS) has been designed with the aim of making it easier for novices to learn the PHP language in order to develop dynamic web pages. Programming requires practice. This makes it necessary to include practical exercises in any ITS that supports students learning to program. The PHP ITS works by providing exercises for students to solve and then providing feedback based on their solutions. The major challenge here is to be able to identify many semantically equivalent solutions to a single exercise. The PHP ITS achieves this by using theories of Artificial Intelligence (AI) including first-order predicate logic and classical and hierarchical planning to model the subject matter taught by the system. This paper highlights the approach taken by the PHP ITS to analyse students’ programs that include a number of program constructs that are used by beginners of web development. The PHP ITS was built using this model and evaluated in a unit at the Queensland University of Technology. The results showed that it was capable of correctly analysing over 96 % of the solutions to exercises supplied by students.

Sectionalizing strategies for minimizing outage durations of critical loads in parallel power system restoration with bi-level programming

Fast restoration of critical loads and non-black-start generators can significantly reduce the economic losses caused by power system blackouts. In a parallel power system restoration scenario, the sectionalization of restoration subsystems plays a very important role in determining the pickup of critical loads before synchronization. Most existing research mainly focuses on the startup of non-black-start generators. The restoration of critical loads, especially the loads with cold load characteristics, has not yet been addressed in optimizing the subsystem divisions. As a result, sectionalized restoration subsystems cannot achieve the best coordination between the pickup of loads and the ramping of generators. In order to generate sectionalizing strategies considering the pickup of critical loads in parallel power system restoration scenarios, an optimization model considering power system constraints, the characteristics of the cold load pickup and the features of generator startup is proposed in this paper. A bi-level programming approach is employed to solve the proposed sectionalizing model. In the upper level the optimal sectionalizing problem for the restoration subsystems is addressed, while in the lower level the objective is to minimize the outage durations of critical loads. The proposed sectionalizing model has been validated by the New-England 39-bus system and the IEEE 118-bus system. Further comparisons with some existing methods are carried out as well.

Programming: Reading, writing and reversing

In this paper, we look at the concept of reversibility, that is, negating opposites, counterbalances, and actions that can be reversed. Piaget identified reversibility as an indicator of the ability to reason at a concrete operational level. We investigate to what degree novice programmers manifest the ability to work with this concept of reversibility by providing them with a small piece of code and then asking them to write code that undoes the effect of that code. On testing entire cohorts of students in their first year of learning to program, we found an overwhelming majority of them could not cope with such a concept. We then conducted think aloud studies of novices where we observed them working on this task and analyzed their contrasting abilities to deal with it. The results of this study demonstrate the need for better understanding our students' reasoning abilities, and a teaching model aimed at that level of reality.

A linear programming approach for estimating the structure of a sparse linear genetic network from transcript profiling data

Background: A genetic network can be represented as a directed graph in which a node corresponds to a gene and a directed edge specifies the direction of influence of one gene on another. The reconstruction of such networks from transcript profiling data remains an important yet challenging endeavor. A transcript profile specifies the abundances of many genes in a biological sample of interest. Prevailing strategies for learning the structure of a genetic network from high-dimensional transcript profiling data assume sparsity and linearity. Many methods consider relatively small directed graphs, inferring graphs with up to a few hundred nodes. This work examines large undirected graphs representations of genetic networks, graphs with many thousands of nodes where an undirected edge between two nodes does not indicate the direction of influence, and the problem of estimating the structure of such a sparse linear genetic network (SLGN) from transcript profiling data. Results: The structure learning task is cast as a sparse linear regression problem which is then posed as a LASSO (l1-constrained fitting) problem and solved finally by formulating a Linear Program (LP). A bound on the Generalization Error of this approach is given in terms of the Leave-One-Out Error. The accuracy and utility of LP-SLGNs is assessed quantitatively and qualitatively using simulated and real data. The Dialogue for Reverse Engineering Assessments and Methods (DREAM) initiative provides gold standard data sets and evaluation metrics that enable and facilitate the comparison of algorithms for deducing the structure of networks. The structures of LP-SLGNs estimated from the INSILICO1, INSILICO2 and INSILICO3 simulated DREAM2 data sets are comparable to those proposed by the first and/or second ranked teams in the DREAM2 competition. The structures of LP-SLGNs estimated from two published Saccharomyces cerevisae cell cycle transcript profiling data sets capture known regulatory associations. In each S. cerevisiae LP-SLGN, the number of nodes with a particular degree follows an approximate power law suggesting that its degree distributions is similar to that observed in real-world networks. Inspection of these LP-SLGNs suggests biological hypotheses amenable to experimental verification. Conclusion: A statistically robust and computationally efficient LP-based method for estimating the topology of a large sparse undirected graph from high-dimensional data yields representations of genetic networks that are biologically plausible and useful abstractions of the structures of real genetic networks. Analysis of the statistical and topological properties of learned LP-SLGNs may have practical value; for example, genes with high random walk betweenness, a measure of the centrality of a node in a graph, are good candidates for intervention studies and hence integrated computational – experimental investigations designed to infer more realistic and sophisticated probabilistic directed graphical model representations of genetic networks. The LP-based solutions of the sparse linear regression problem described here may provide a method for learning the structure of transcription factor networks from transcript profiling and transcription factor binding motif data.

Generalized Query-By-Rule: a heterogeneous database query language

This paper describes the design and implementation of a high-level query language called Generalized Query-By-Rule (GQBR) which supports retrieval, insertion, deletion and update operations. This language, based on the formalism of database logic, enables the users to access each database in a distributed heterogeneous environment, without having to learn all the different data manipulation languages. The compiler has been implemented on a DEC 1090 system in Pascal.

Reasoning about natural language phrases for semantic goal driven exploration

This paper presents a symbolic navigation system that uses spatial language descriptions to inform goal-directed exploration in unfamiliar office environments. An abstract map is created from a collection of natural language phrases describing the spatial layout of the environment. The spatial representation in the abstract map is controlled by a constraint based interpretation of each natural language phrase. In goal-directed exploration of an unseen office environment, the robot links the information in the abstract map to observed symbolic information and its grounded world representation. This paper demonstrates the ability of the system, in both simulated and real-world trials, to efficiently find target rooms in environments that it has never been to previously. In three unexplored environments, it is shown that on average the system travels only 8.42% further than the optimal path when using only natural language phrases to complete navigation tasks.