Parallel and distributed computing with Java

The Java language first came to public attention in 1995. Within a year, it was being speculated that Java may be a good language for parallel and distributed computing. Its core features, including being objected oriented and platform independence, as well as having built-in network support and threads, has encouraged this view. Today, Java is being used in almost every type of computer-based system, ranging from sensor networks to high performance computing platforms, and from enterprise applications through to complex research-based.simulations. In this paper the key features that make Java a good language for parallel and distributed computing are first discussed. Two Java-based middleware systems, namely MPJ Express, an MPI-like Java messaging system, and Tycho, a wide-area asynchronous messaging framework with an integrated virtual registry are then discussed. The paper concludes by highlighting the advantages of using Java as middleware to support distributed applications.

Example uses of a learning object

A self study course for learning to program using the C programming language has been developed. A Learning Object approach was used in the design of the course. One of the benefits of the Learning Object approach is that the learning material can be reused for different purposes. 'Me course developed is designed so that learners can choose the pedagogical approach most suited to their personal learning requirements. For all learning approaches a set of common Assessment Learning Objects (ALOs or tests) have been created. The design of formative assessments with ALOs can be carried out by the Instructional Designer grouping ALOs to correspond to a specific assessment intention. The course is non-credit earning, so there is no summative assessment, all assessment is formative. In this paper examples of ALOs and their uses is presented together with their uses as decided by the Instructional Designer and learner. Personalisation of the formative assessment of skills can be decided by the Instructional Designer or the learner using a repository of pre-designed ALOs. The process of combining ALOs can be carried out manually or in a semi-automated way using metadata that describes the ALO and the skill it is designed to assess.

Factors mediating the routinisation of e-learning within a traditional university education environment

Technology-enhanced or Computer Aided Learning (e-learning) can be institutionally integrated and supported by learning management systems or Virtual Learning Environments (VLEs) to offer efficiency gains, effectiveness and scalability of the e-leaning paradigm. However this can only be achieved through integration of pedagogically intelligent approaches and lesson preparation tools environment and VLE that is well accepted by both the students and teachers. This paper critically explores some of the issues relevant to scalable routinisation of e-learning at the tertiary level, typically first year university undergraduates, with the teaching of Relational Data Analysis (RDA), as supported by multimedia authoring, as a case study. The paper concludes that blended learning approaches which balance the deployment of e-learning with other modalities of learning delivery such as instructor–mediated group learning etc offer the most flexible and scalable route to e-learning but that this requires the graceful integration of platforms for multimedia production, distribution and delivery through advanced interactive spaces that provoke learner engagement and promote learning autonomy and group learning facilitated by a cooperative-creative learning environment that remains open to personal exploration of constructivist-constructionist pathways to learning.

Self-organising neural networks for adaptive control

Self-organizing neural networks have been implemented in a wide range of application areas such as speech processing, image processing, optimization and robotics. Recent variations to the basic model proposed by the authors enable it to order state space using a subset of the input vector and to apply a local adaptation procedure that does not rely on a predefined test duration limit. Both these variations have been incorporated into a new feature map architecture that forms an integral part of an Hybrid Learning System (HLS) based on a genetic-based classifier system. Problems are represented within HLS as objects characterized by environmental features. Objects controlled by the system have preset targets set against a subset of their features. The system's objective is to achieve these targets by evolving a behavioural repertoire that efficiently explores and exploits the problem environment. Feature maps encode two types of knowledge within HLS — long-term memory traces of useful regularities within the environment and the classifier performance data calibrated against an object's feature states and targets. Self-organization of these networks constitutes non-genetic-based (experience-driven) learning within HLS. This paper presents a description of the HLS architecture and an analysis of the modified feature map implementing associative memory. Initial results are presented that demonstrate the behaviour of the system on a simple control task.

Using self-organising feature maps for the control of artificial organisms

Variations on the standard Kohonen feature map can enable an ordering of the map state space by using only a limited subset of the complete input vector. Also it is possible to employ merely a local adaptation procedure to order the map, rather than having to rely on global variables and objectives. Such variations have been included as part of a hybrid learning system (HLS) which has arisen out of a genetic-based classifier system. In the paper a description of the modified feature map is given, which constitutes the HLSs long term memory, and results in the control of a simple maze running task are presented, thereby demonstrating the value of goal related feedback within the overall network.

The system identification and control of Hammerstein system using non-uniform rational B-spline neural network and particle swarm optimization

In this paper a new system identification algorithm is introduced for Hammerstein systems based on observational input/output data. The nonlinear static function in the Hammerstein system is modelled using a non-uniform rational B-spline (NURB) neural network. The proposed system identification algorithm for this NURB network based Hammerstein system consists of two successive stages. First the shaping parameters in NURB network are estimated using a particle swarm optimization (PSO) procedure. Then the remaining parameters are estimated by the method of the singular value decomposition (SVD). Numerical examples including a model based controller are utilized to demonstrate the efficacy of the proposed approach. The controller consists of computing the inverse of the nonlinear static function approximated by NURB network, followed by a linear pole assignment controller.

Does SMS text messaging help or harm adults’ knowledge of standard spelling?

The recent increase in short messaging system (SMS) text messaging, often using abbreviated, non-conventional ‘textisms’ (e.g. ‘2nite’), in school-aged children has raised fears of negative consequences of such technology for literacy. The current research used a paradigm developed by Dixon and Kaminska, who showed that exposure to phonetically plausible misspellings (e.g. ‘recieve’) negatively affected subsequent spelling performance, though this was true only with adults, not children. The current research extends this work to directly investigate the effects of exposure to textisms, misspellings and correctly spelledwords on adults’ spelling. Spelling of a set of key words was assessed both before and after an exposure phase where participants read the same key words, presented either as textisms (e.g. ‘2nite’), correctly spelled (e.g. ‘tonight’) or misspelled (e.g. 'tonite’)words. Analysis showed that scores decreased from pre- to post-test following exposure to misspellings, whereas performance improved following exposure to correctly spelled words and, interestingly, to textisms. Data suggest that exposure to textisms, unlike misspellings, had a positive effect on adults’ spelling. These findings are interpreted in light of other recent research suggesting a positive relationship between texting and some literacy measures in school-aged children.

Haptics in medicine and clinical skill acquisition

THE clinical skills of medical professionals rely strongly on the sense of touch, combined with anatomical and diagnostic knowledge. Haptic exploratory procedures allow the expert to detect anomalies via gross and fine palpation, squeezing, and contour following. Haptic feedback is also key to medical interventions, for example when an anaesthetist inserts an epidural needle, a surgeon makes an incision, a dental surgeon drills into a carious lesion, or a veterinarian sutures a wound. Yet, current trends in medical technology and training methods involve less haptic feedback to clinicians and trainees. For example, minimally invasive surgery removes the direct contact between the patient and clinician that gives rise to natural haptic feedback, and furthermore introduces scaling and rotational transforms that confuse the relationship between movements of the hand and the surgical site. Similarly, it is thought that computer-based medical simulation and training systems require high-resolution and realistic haptic feedback to the trainee for significant training transfer to occur. The science and technology of haptics thus has great potential to affect the performance of medical procedures and learning of clinical skills. This special section is about understanding