Keystroke patterns classification using the ARTMAP-FD neural network

This paper presents the development of a keystroke dynamics-based user authentication system using the ARTMAP-FD neural network. The effectiveness of ARTMAPFD in classifying keystroke patterns is analyzed and compared against a number of widely used machine learning systems. The results show that ARTMAP-FD performs well against many of its counterparts in keystroke patterns classification. Apart from that, instead of using the conventional typing timing characteristics, the applicability of typing pressure to ascertaining user's identity is investigated. The experimental results show that combining both latency and pressure patterns can improve the Equal Error Rate (ERR) of the system.

Application of an adaptive neural network with symbolic rule extraction to fault detection and diagnosis in a power generation plant

Artificial neural networks have a good potential to be employed for fault diagnosis and condition monitoring problems in complex processes. In this paper, the applicability of the fuzzy ARTMAP (FAM) neural network as an intelligent learning system for fault detection and diagnosis in a power generation plant is described. The process under scrutiny is the circulating water (CW) system, with specific attention to the conditions of heat transfer and tube blockage in the CW system. A series of experiments has been conducted systematically to investigate the effectiveness of FAM in fault detection and diagnosis tasks. In addition, a set of domain rules has been extracted from the trained FAM network so that its predictions can be explained and justified. The outcomes demonstrate the benefits of employing FAM as an intelligent fault detection and diagnosis tool with an explanatory capability for monitoring and diagnosing complex processes in power generation plants.

Condition monitoring and fault prediction via an adaptive neural network

This paper describes the application of an adaptive neural network, called Fuzzy ARTMAP (FAM), to handle fault prediction and condition monitoring problems in a power generation station. The FAM network, which is supplemented with a pruning algorithm, is used as a classifier to predict different machine conditions, in an off-line learning mode. The process under scrutiny in the power plant is the Circulating Water (CW) system, with prime attention to monitoring the heat transfer efficiency of the condensers. Several phases of experiments were conducted to investigate the `optimum' setting of a set of parameters of the FAM classifier for monitoring heat transfer conditions in the power plant.

Improved GART neural network model for pattern classification and rule extraction with application to power systems

Generalized adaptive resonance theory (GART) is a neural network model that is capable of online learning and is effective in tackling pattern classification tasks. In this paper, we propose an improved GART model (IGART), and demonstrate its applicability to power systems. IGART enhances the dynamics of GART in several aspects, which include the use of the Laplacian likelihood function, a new vigilance function, a new match-tracking mechanism, an ordering algorithm for determining the sequence of training data, and a rule extraction capability to elicit if-then rules from the network. To assess the effectiveness of IGART and to compare its performances with those from other methods, three datasets that are related to power systems are employed. The experimental results demonstrate the usefulness of IGART with the rule extraction capability in undertaking classification problems in power systems engineering.

Managing manufacturing technology investments: An intelligent learning system approach

This paper proposes an intelligent decision-support system for managing manufacturing technology investments. The intelligent system is a hybrid integration of two information processing modules: case-based reasoning and fuzzy ARTMAP – a supervised adaptive resonance theory (ART) neural network with a multi-dimensional map. The developed system captures a company's strategic information, provides facilities to quantify qualitative attributes and analyses them alongside the quantitative attributes in an evaluation framework. Through the system, similar cases can be retrieved to enable managers to make effective use of their knowledge and experience of previously delivered technologies and projects as an input to the prioritization of future projects. Other salient features of the system include its ability to adapt and absorb new knowledge and responses pertaining to significant events in the business environment, as well as to extract and elucidate information from the knowledge database for explaining and justifying its analysis. The applicability of the developed system is evaluated using a real case study in collaboration with a pharmaceutical manufacturing firm.

Online pattern classification with multiple neural network systems: an experimental study

In this paper, an empirical study of the development and application of a committee of neural networks on online pattern classification tasks is presented. A multiple classifier framework is designed by adopting an Adaptive Resonance Theory-based (ART) autonomously learning neural network as the building block. A number of algorithms for combining outputs from multiple neural classifiers are considered, and two benchmark data sets have been used to evaluate the applicability of the proposed system. Different learning strategies coupling offline and online learning approaches, as well as different input pattern representation schemes, including the "ensemble" and "modular" methods, have been examined experimentally. Benefits and shortcomings of each approach are systematically analyzed and discussed. The results are comparable, and in some cases superior, with those from other classification algorithms. The experiments demonstrate the potentials of the proposed multiple neural network systems in offering an alternative to handle online pattern classification tasks in possibly nonstationary environments.

Learning to think, learning to learn : super teaching for super success

The early childhood years are a busy, exciting time. New discoveries, skills and competencies are a regular part of life for a young child. Early childhood teachers have the opportunity to optimise these amazing and important years. In this paper, I will discuss teaching strategies that can turn children’s possibilities into realities. The practices that will be discussed involve expanding thinking, problem-solving and developing hypotheses. These teaching strategies can build on children’s learning dispositions and their strengths and interests to put the ‘wow factor’ into learning.

Boosting the performance of the fuzzy min-max neural network in pattern classification tasks

In this paper, a boosted Fuzzy Min-Max Neural Network (FMM) is proposed. While FMM is a learning algorithm which is able to learn new classes and to refine existing classes incrementally, boosting is a general method for improving accuracy of any learning algorithm. In this work, AdaBoost is applied to improve the performance of FMM when its classification results deteriorate from a perfect score. Two benchmark databases are used to assess the applicability of boosted FMM, and the results are compared with those from other approaches. In addition, a medical diagnosis task is employed to assess the effectiveness of boosted FMM in a real application. All the experimental results consistently demonstrate that the performance of FMM can be considerably improved when boosting is deployed.

Online peer mentoring with pre-service teachers : a learning experience

In 2010, 34 pre-service teachers at Deakin University were invited to use Web 2.0 technologies to support practicum in rural and regional schools. Students in their final year of the Bachelor of Education Primary course were given access to an online forum, a ‗ning‘, to facilitate development of mentoring relationships within a community of peers. Access to the ning was presented as an optional extra available only to students undertaking their professional experience in rural and regional settings. Based on the work of Le Cornu (2005), mentoring was framed as a collaborative and collegial arrangement through which participants could hone the interpersonal and critical reflection skills crucial to practicum. A ning was selected as it: 1) allowed the creation of a closed, protected social network with customised options, and 2) requires little technological skills and investment of time from participants in terms of setting up a profile and participating in the online community. These features seemed to make it an ideal platform for pre-service teachers to analyse and reflect on professional experience. However, the small pre-service cohort did not choose to access the site. This unexpected outcome seems to challenge contemporary discourses about the current generation‘s attitudes to web based technology. It also highlights the importance of coupling use of template-based online tools, such as the ning, with awareness of Bourdieu‘s (1977) social capital to ensure uptake. In capturing the learnings from the project and systematically reviewing relevant literature, this paper provides a set of recommendations for conceptualising and engaging pre-service teachers in the use of online forums.

Learning as knowledge networking : conceptual foundations for revised uses of the internet in higher education

This paper argues that the inherent characteristics of knowledge work, when combined with the operation of the Internet in contemporary society, produce a change in the dominant paradigm of what constitutes knowledge work. Since learning is a form of knowledge work, therefore this change will affect university education. The paper further argues that, because of the way in which online learning initially developed in universities, in most cases, the current approach to the Internet and higher education does not account for the changed conditions of knowledge in a network society. It concludes that new directions are needed which will allow us to make technology and pedagogy choices for future education better suited to a network society.

Virtual design studio within a blended social network

Online interactions, multimedia, mobile computing and face-to-face learning create blended learning environments to which some Virtual Design Studios (VDS) have reacted. Social Networks (SN), as instruments for communication, have provided a potentially fruitful operative base for VDS. These technologies transfer communication, leadership, democratic interaction, teamwork, social engagement and responsibility away from the design tutors to the participants. The implementation of a Social Network VDS (SNVDS) moved the VDS beyond its conventional realm and enabled students to develop architectural design that is embedded into a community of learners and their expertise both online and offline. Problem-based learning (PBL) becomes an iterative and reflexive process facilitating deep learning. The paper discusses details of the SNVDS, its pedagogical implications to PBL, and presents how the SNVDS is successful in empowering architectural students to collaborate and communicate design proposals that integrate a variety of skills, deep learning, knowledge and construction with a rich learning experience.

Wonsa-ponna time. His stories, her stories and my stories : stories about teaching and learning

The article provides an example of a teaching story, embedded in a child’s learning story, makes connections to teacher identities and discusses the way that teachers can increase their professional self-awareness through critiquing their practice.

Network traffic classification using correlation information

Traffic classification has wide applications in network management, from security monitoring to quality of service measurements. Recent research tends to apply machine learning techniques to flow statistical feature based classification methods. The nearest neighbor (NN)-based method has exhibited superior classification performance. It also has several important advantages, such as no requirements of training procedure, no risk of overfitting of parameters, and naturally being able to handle a huge number of classes. However, the performance of NN classifier can be severely affected if the size of training data is small. In this paper, we propose a novel nonparametric approach for traffic classification, which can improve the classification performance effectively by incorporating correlated information into the classification process. We analyze the new classification approach and its performance benefit from both theoretical and empirical perspectives. A large number of experiments are carried out on two real-world traffic data sets to validate the proposed approach. The results show the traffic classification performance can be improved significantly even under the extreme difficult circumstance of very few training samples.