Rail incident investigator training: Is there support for a standardised Australasian training framework?

Background: Historically rail organisations have been operating in silos and devising their own training agendas. However with the harmonisation of the Australian workplace health and safety legislation and the appointment of a national rail safety regulator in 2013, rail incident investigator experts are exploring the possibility of developing a unified approach to investigator training. Objectives: The Australian CRC for Rail Innovation commissioned a training needs analysis to identify if common training needs existed between organisations and to assess support for the development of a national competency framework for rail incident investigations. Method: Fifty-two industry experts were consulted to explore the possibility of the development of a standardised training framework. These experts were sourced from within 19 Australasian organisations, comprising Rail Operators and Regulators in Queensland, New South Wales, Victoria, Western Australia, South Australia and New Zealand. Results: Although some competency requirements appear to be organisation specific, the vast majority of reported training requirements were generic across the Australasian rail operators and regulators. Industry experts consistently reported strong support for the development of a national training framework. Significance: The identification of both generic training requirements across organisations and strong support for standardised training indicates that the rail industry is receptive to the development of a structured training framework. The development of an Australasian learning framework could: increase efficiency in course development and reduce costs; establish recognised career pathways; and facilitate consistency with regards to investigator training.

Locally weighted learning model predictivek control for elastic joint robots

This paper proposes an efficient and online learning control system that uses the successful Model Predictive Control (MPC) method in a model based locally weighted learning framework. The new approach named Locally Weighted Learning Model Predictive Control (LWL-MPC) has been proposed as a solution to learn to control complex and nonlinear Elastic Joint Robots (EJR). Elastic Joint Robots are generally difficult to learn to control due to their elastic properties preventing standard model learning techniques from being used, such as learning computed torque control. This paper demonstrates the capability of LWL-MPC to perform online and incremental learning while controlling the joint positions of a real three Degree of Freedom (DoF) EJR. An experiment on a real EJR is presented and LWL-MPC is shown to successfully learn to control the system to follow two different figure of eight trajectories.

Locally weighted learning model predictive control for nonlinear and time varying dynamics

This paper proposes an online learning control system that uses the strategy of Model Predictive Control (MPC) in a model based locally weighted learning framework. The new approach, named Locally Weighted Learning Model Predictive Control (LWL-MPC), is proposed as a solution to learn to control robotic systems with nonlinear and time varying dynamics. This paper demonstrates the capability of LWL-MPC to perform online learning while controlling the joint trajectories of a low cost, three degree of freedom elastic joint robot. The learning performance is investigated in both an initial learning phase, and when the system dynamics change due to a heavy object added to the tool point. The experiment on the real elastic joint robot is presented and LWL-MPC is shown to successfully learn to control the system with and without the object. The results highlight the capability of the learning control system to accommodate the lack of mechanical consistency and linearity in a low cost robot arm.

Professional conversations : a collaborative approach to support policy implementation, professional learning and practice change in ECEC

In 2012, Australia introduced a new National Quality Framework, comprising enhanced quality expectations for early childhood education and care services, two national learning frameworks and a new Assessment and Rating System spanning child care centres, kindergartens and preschools, family day care and outside school hours care. This is the linchpin in a series of education reforms designed to support increased access to higher quality early childhood education and care (ECEC) and successful transition to school. As with any policy change, success in real terms relies upon building shared understanding and the capacity of educators to apply new knowledge and to support change and improved practice within their service. With this in mind, a collaborative research project investigated the efficacy of a new approach to professional learning in ECEC: the professional conversation. This paper reports on the trial and evaluation of a series of professional conversations to support implementation of one element of the NQF, the Early Years Learning Framework (DEEWR,2009), and their capacity to promote collaborative reflective practice, shared understanding, and improved practice in ECEC. Set against the backdrop of the NQF, this paper details the professional conversation approach, key challenges and critical success factors, and the learning outcomes for conversation participants. Findings support the efficacy of this approach to professional learning in ECEC, and its capacity to support policy reform and practice change in ECEC.

Informed systems approach : new directions for organizational learning

The Informed Systems Approach offers models for advancing workplace learning within collaboratively designed systems that promote using information to learn through collegial exchange and reflective dialogue. This systemic approach integrates theoretical antecedents and process models, including the learning theories of Peter Checkland (Soft Systems Methodology), which advance systems design and informed action, and Christine Bruce (informed learning), which generate information experiences and professional practices. Ikujiro Nonaka’s systems ideas (SECI model) and Mary Crossan’s learning framework (4i framework) further animate workplace knowledge creation through learning relationships engaging individuals with ideas.

Factors influencing robustness and effectiveness of conditional random fields in active learning frameworks

Active learning approaches reduce the annotation cost required by traditional supervised approaches to reach the same effectiveness by actively selecting informative instances during the learning phase. However, effectiveness and robustness of the learnt models are influenced by a number of factors. In this paper we investigate the factors that affect the effectiveness, more specifically in terms of stability and robustness, of active learning models built using conditional random fields (CRFs) for information extraction applications. Stability, defined as a small variation of performance when small variation of the training data or a small variation of the parameters occur, is a major issue for machine learning models, but even more so in the active learning framework which aims to minimise the amount of training data required. The factors we investigate are a) the choice of incremental vs. standard active learning, b) the feature set used as a representation of the text (i.e., morphological features, syntactic features, or semantic features) and c) Gaussian prior variance as one of the important CRFs parameters. Our empirical findings show that incremental learning and the Gaussian prior variance lead to more stable and robust models across iterations. Our study also demonstrates that orthographical, morphological and contextual features as a group of basic features play an important role in learning effective models across all iterations.

Moving forwards with the aim of going backwards fast : high performance rowing as a learning environment

Background and Purpose: - This paper focuses on the learning culture within the high performance levels of rowing. In doing so, we explore the case of an individual’s learning as he moves across athletic, coaching and administrative functions. This exploration draws on a cultural learning framework and complementary theorisings related to reflexivity. Method - This study makes use of an intellectually, morally and collaboratively challenging approach whereby one member of the research team was also the sole participant of this study. The participant’s careers as a high performance athlete, coach and administrator, coupled with his experience in conducting empirical research presented a rare opportunity to engage in collaborative research (involving degrees of insider and outsider status for each of the research team). We acknowledge that others have looked to combine roles of coach / athlete / administrator with that of researcher however few (if any) have attempted to combine them all in one project. Moreover, coupled with the approach to reflexivity adopted in this study and the authorship contributions we consider this scholarly direction uncommon. Data were comprised of recorded research conversations, a subsequently constructed learning narrative, reflections on the narrative, a stimulated reflective piece from the participant, and a final (re)construction of the participant’s story. Accordingly, data were integrated through an iterative process of thematic analysis. Results - The cultural (i.e., the ways things get done) and structural (e.g., the rules and regulations) properties of high performance rowing were found to shape both the opportunities to be present (e.g., secure a place in the crew) and to learn (e.g., learn the skills required to perform at an Olympic level). However, the individual’s personal properties were brought to bear on re-shaping the constraints such that many limitations could be overcome. In keeping with the theory of learning cultures, the culture of rowing was found to position individuals (a coxswain in this case) differentially. In a similar manner, a range of structural features was found to be important in shaping the cultural and personal elements in performance contexts. For example, the ‘field of play’ was found to be important as a structural feature (i.e., inability of coach to communicate with athletes) in shaping the cultural and personal elements of learning in competition (e.g., positioning the coxswain as an in-boat coach and trusted crewmate). Finally, the cultural and structural elements in rowing appeared to be activated by the participant’s personal elements, most notably his orientation towards quality performance. Conclusion - The participant in this study was found to be driven by the project that he cares about most and at each turn he has bent his understanding of his sport back on itself to see if he can find opportunities to learn and subsequently explore ways to improve performance. The story here emphasises the importance of learner agency, and this is an aspect that has often been missing in recent theorising about learning. In this study, we find an agent using his ‘personal emergent powers to activate the resources in the culture and structure of his sport in an attempt to improve performance. We conclude from this account that this particular high performance rowing culture is one that provided support but nonetheless encouraged those involved, to ‘figure things out’ for themselves – be it as athletes, coaches and/or administrators.

Analyzing the impact of learning inputs on near-to-far terrain traversability estimation

With the increasing need to adapt to new environments, data-driven approaches have been developed to estimate terrain traversability by learning the rover’s response on the terrain based on experience. Multiple learning inputs are often used to adequately describe the various aspects of terrain traversability. In a complex learning framework, it can be difficult to identify the relevance of each learning input to the resulting estimate. This paper addresses the suitability of each learning input by systematically analyzing the impact of each input on the estimate. Sensitivity Analysis (SA) methods provide a means to measure the contribution of each learning input to the estimate variability. Using a variance-based SA method, we characterize how the prediction changes as one or more of the input changes, and also quantify the prediction uncertainty as attributed from each of the inputs in the framework of dependent inputs. We propose an approach built on Analysis of Variance (ANOVA) decomposition to examine the prediction made in a near-to-far learning framework based on multi-task GP regression. We demonstrate the approach by analyzing the impact of driving speed and terrain geometry on the prediction of the rover’s attitude and chassis configuration in a Marsanalogue terrain using our prototype rover Mawson.

The ‘state of play’ in Australia: Early childhood educators and play-based learning

This article provides an overview of the Education Meets Play study that will investigate early childhood educators’ use of play-based learning, now mandatory under the National Quality Standard. By building on what can be gleaned about educators’ approaches to play-based learning prior to the implementation of the Early Years Learning Framework, the study will contribute to the evidence base concerning the implementation and effects of Australia’s early childhood education and care policy reform initiatives.

Feeling the pressure: Early childhood educators’ reported views about learning and teaching phonics in Australian prior-to-school settings

Early childhood educators’ beliefs about literacy teaching can impact on the types of phonics experiences educators provide for children in prior-to-school settings. The Australian Early Years Learning Framework supports a play-based, intentional approach to teaching phonics, however little is known about what Australian early childhood educators believe is important in teaching phonics in the prior-to-school years. Using a qualitative content analysis, this research study investigates 115 early childhood educators’ views about how phonics should be taught and the use of commercially produced phonics programs (e.g. Jolly Phonics and Letterland) in prior-to-school settings. This study further investigates educators’ perceived pressures to include structured phonics lessons, as a way of addressing parental notions of ‘school readiness’. The results of this study indicate conflicting views were held about how phonics should be taught. Some educators also experienced external pressures to engage in literacy practices that may be in opposition with their own beliefs about how literacy is learnt. This study provides insights into the pedagogical practices early childhood educators believe are appropriate when teaching phonics. The educational implications are discussed.

Active learning: A step towards automating medical concept extraction

Objective This paper presents an automatic active learning-based system for the extraction of medical concepts from clinical free-text reports. Specifically, (1) the contribution of active learning in reducing the annotation effort, and (2) the robustness of incremental active learning framework across different selection criteria and datasets is determined. Materials and methods The comparative performance of an active learning framework and a fully supervised approach were investigated to study how active learning reduces the annotation effort while achieving the same effectiveness as a supervised approach. Conditional Random Fields as the supervised method, and least confidence and information density as two selection criteria for active learning framework were used. The effect of incremental learning vs. standard learning on the robustness of the models within the active learning framework with different selection criteria was also investigated. Two clinical datasets were used for evaluation: the i2b2/VA 2010 NLP challenge and the ShARe/CLEF 2013 eHealth Evaluation Lab. Results The annotation effort saved by active learning to achieve the same effectiveness as supervised learning is up to 77%, 57%, and 46% of the total number of sequences, tokens, and concepts, respectively. Compared to the Random sampling baseline, the saving is at least doubled. Discussion Incremental active learning guarantees robustness across all selection criteria and datasets. The reduction of annotation effort is always above random sampling and longest sequence baselines. Conclusion Incremental active learning is a promising approach for building effective and robust medical concept extraction models, while significantly reducing the burden of manual annotation.

Structured Learning for Non-Smooth Ranking losses

Learning to rank from relevance judgment is an active research area. Itemwise score regression, pairwise preference satisfaction, and listwise structured learning are the major techniques in use. Listwise structured learning has been applied recently to optimize important non-decomposable ranking criteria like AUC (area under ROC curve) and MAP(mean average precision). We propose new, almost-lineartime algorithms to optimize for two other criteria widely used to evaluate search systems: MRR (mean reciprocal rank) and NDCG (normalized discounted cumulative gain)in the max-margin structured learning framework. We also demonstrate that, for different ranking criteria, one may need to use different feature maps. Search applications should not be optimized in favor of a single criterion, because they need to cater to a variety of queries. E.g., MRR is best for navigational queries, while NDCG is best for informational queries. A key contribution of this paper is to fold multiple ranking loss functions into a multi-criteria max-margin optimization.The result is a single, robust ranking model that is close to the best accuracy of learners trained on individual criteria. In fact, experiments over the popular LETOR and TREC data sets show that, contrary to conventional wisdom, a test criterion is often not best served by training with the same individual criterion.

A neural network based automatic generation control design through reinforcement learning

This paper presents the design and implementation of a learning controller for the Automatic Generation Control (AGC) in power systems based on a reinforcement learning (RL) framework. In contrast to the recent RL scheme for AGC proposed by us, the present method permits handling of power system variables such as Area Control Error (ACE) and deviations from scheduled frequency and tie-line flows as continuous variables. (In the earlier scheme, these variables have to be quantized into finitely many levels). The optimal control law is arrived at in the RL framework by making use of Q-learning strategy. Since the state variables are continuous, we propose the use of Radial Basis Function (RBF) neural networks to compute the Q-values for a given input state. Since, in this application we cannot provide training data appropriate for the standard supervised learning framework, a reinforcement learning algorithm is employed to train the RBF network. We also employ a novel exploration strategy, based on a Learning Automata algorithm,for generating training samples during Q-learning. The proposed scheme, in addition to being simple to implement, inherits all the attractive features of an RL scheme such as model independent design, flexibility in control objective specification, robustness etc. Two implementations of the proposed approach are presented. Through simulation studies the attractiveness of this approach is demonstrated.

Boosting-based transfer learning for multi-view head-pose classification from surveillance videos

This work proposes a boosting-based transfer learning approach for head-pose classification from multiple, low-resolution views. Head-pose classification performance is adversely affected when the source (training) and target (test) data arise from different distributions (due to change in face appearance, lighting, etc). Under such conditions, we employ Xferboost, a Logitboost-based transfer learning framework that integrates knowledge from a few labeled target samples with the source model to effectively minimize misclassifications on the target data. Experiments confirm that the Xferboost framework can improve classification performance by up to 6%, when knowledge is transferred between the CLEAR and FBK four-view headpose datasets.

NESTED SPARSE BAYESIAN LEARNING FOR BLOCK-SPARSE SIGNALS WITH INTRA-BLOCK CORRELATION

In this work, we address the recovery of block sparse vectors with intra-block correlation, i.e., the recovery of vectors in which the correlated nonzero entries are constrained to lie in a few clusters, from noisy underdetermined linear measurements. Among Bayesian sparse recovery techniques, the cluster Sparse Bayesian Learning (SBL) is an efficient tool for block-sparse vector recovery, with intra-block correlation. However, this technique uses a heuristic method to estimate the intra-block correlation. In this paper, we propose the Nested SBL (NSBL) algorithm, which we derive using a novel Bayesian formulation that facilitates the use of the monotonically convergent nested Expectation Maximization (EM) and a Kalman filtering based learning framework. Unlike the cluster-SBL algorithm, this formulation leads to closed-form EMupdates for estimating the correlation coefficient. We demonstrate the efficacy of the proposed NSBL algorithm using Monte Carlo simulations.