Applying incremental EM to Bayesian classifiers in the learning of hyperspectral remote sensing data

In this paper, we apply the incremental EM method to Bayesian Network Classifiers to learn and interpret hyperspectral sensor data in robotic planetary missions. Hyperspectral image spectroscopy is an emerging technique for geological investigations from airborne or orbital sensors. Many spacecraft carry spectroscopic equipment as wavelengths outside the visible light in the electromagnetic spectrum give much greater information about an object. The algorithm used is an extension to the standard Expectation Maximisation (EM). The incremental method allows us to learn and interpret the data as they become available. Two Bayesian network classifiers were tested: the Naive Bayes, and the Tree-Augmented-Naive Bayes structures. Our preliminary experiments show that incremental learning with unlabelled data can improve the accuracy of the classifier.

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.

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.

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.

Modelling, collaboration and integration : a case study for the delivery of public buildings

An evolution in the use of digital modelling has occurred in the Queensland Department of Public Works Division of Project Services over the last 20 years from: the initial implementation of computer aided design and documentation (CADD); to experimentation with building information modelling (BIM); to embedding integrated practice (IP); to current steps towards integrated project delivery (IPD) including the active involvement of consultants and contractors in the design/delivery process. This case study is one of three undertaken through the Australian Sustainable Built Environment National Research Centre investigating past R&D investment. The intent of these cases is to inform the development of policy guidelines for future investment in the construction industry in Australia. This research is informing the activities of CIB Task Group 85 R&D Investment and Impact. The uptake of digital modelling by Project Services has been approached through an incremental learning approach. This has been driven by a strong and clear vision with a focus on developing more efficient delivery mechanisms through the use of new technology coupled with process change. Findings reveal an organisational focus on several areas including: (i) strategic decision making including the empowerment of innovation leaders and champions; (ii) the acquisition and exploitation of knowledge; (iii) product and process development (with a focus on efficiency and productivity); (iv) organisational learning; (v) maximising the use of technology; and (vi) supply chain integration. Key elements of this approach include pilot projects, researcher engagement, industry partnerships and leadership.

Semantic context and visual feature effects in object naming : an fMRI study using arterial spin labeling

Previous behavioral studies reported a robust effect of increased naming latencies when objects to be named were blocked within semantic category, compared to items blocked between category. This semantic context effect has been attributed to various mechanisms including inhibition or excitation of lexico-semantic representations and incremental learning of associations between semantic features and names, and is hypothesized to increase demands on verbal self-monitoring during speech production. Objects within categories also share many visual structural features, introducing a potential confound when interpreting the level at which the context effect might occur. Consistent with previous findings, we report a significant increase in response latencies when naming categorically related objects within blocks, an effect associated with increased perfusion fMRI signal bilaterally in the hippocampus and in the left middle to posterior superior temporal cortex. No perfusion changes were observed in the middle section of the left middle temporal cortex, a region associated with retrieval of lexical-semantic information in previous object naming studies. Although a manipulation of visual feature similarity did not influence naming latencies, we observed perfusion increases in the perirhinal cortex for naming objects with similar visual features that interacted with the semantic context in which objects were named. These results provide support for the view that the semantic context effect in object naming occurs due to an incremental learning mechanism, and involves increased demands on verbal self-monitoring.

A perfusion fMRI investigation of thematic and categorical context effects in the spoken production of object names

The context in which objects are presented influences the speed at which they are named. We employed the blocked cyclic naming paradigm and perfusion functional magnetic resonance imaging (fMRI) to investigate the mechanisms responsible for interference effects reported for thematicallyand categorically related compared to unrelated contexts. Naming objects in categorically homogeneous contexts induced a significant interference effect that accumulated from the second cycle onwards. This interference effect was associated with significant perfusion signal decreases in left middle and posterior lateral temporal cortex and the hippocampus. By contrast, thematically homogeneous contexts facilitated naming latencies significantly in the first cycle and did not differ from heterogeneous contexts thereafter, nor were they associated with any perfusion signal changes compared to heterogeneous contexts. These results are interpreted as being consistent with an account in which the interference effect both originates and has its locus at the lexical level, with an incremental learning mechanism adapting the activation levels of target lexical representations following access. We discuss the implications of these findings for accounts that assume thematic relations can be active lexical competitors or assume mandatory involvement of top-down control mechanisms in interference effects during naming.

Managing open incremental process innovation : absorptive capacity and distributed learning

In this conceptual article, we extend earlier work on Open Innovation and Absorptive Capacity. We suggest that the literature on Absorptive Capacity does not place sufficient emphasis on distributed knowledge and learning or on the application of innovative knowledge. To accomplish physical transformations, organisations need specific Innovative Capacities that extend beyond knowledge management. Accessive Capacity is the ability to collect, sort and analyse knowledge from both internal and external sources. Adaptive Capacity is needed to ensure that new pieces of equipment are suitable for the organisation's own purposes even though they may have been originally developed for other uses. Integrative Capacity makes it possible for a new or modified piece of equipment to be fitted into an existing production process with a minimum of inessential and expensive adjustment elsewhere in the process. These Innovative Capacities are controlled and coordinated by Innovative Management Capacity, a higher-order dynamic capability.

Incremental and inevitable: Contextualising the Threshold Learning Outcomes for law

This article situates the development of the Threshold Learning Outcomes for law in relation to broader national and international trends in legal education and higher education regulation. It also addresses the significance of recent changes to the Australian higher education regulatory landscape catalysed by the current Government's commitment to reducing regulation and red tape for the sector.

Digital kids, analogue students : a mixed methods study of students' engagement with a school-based Web 2.0 learning innovation

The inquiry documented in this thesis is located at the nexus of technological innovation and traditional schooling. As we enter the second decade of a new century, few would argue against the increasingly urgent need to integrate digital literacies with traditional academic knowledge. Yet, despite substantial investments from governments and businesses, the adoption and diffusion of contemporary digital tools in formal schooling remain sluggish. To date, research on technology adoption in schools tends to take a deficit perspective of schools and teachers, with the lack of resources and teacher ‘technophobia’ most commonly cited as barriers to digital uptake. Corresponding interventions that focus on increasing funding and upskilling teachers, however, have made little difference to adoption trends in the last decade. Empirical evidence that explicates the cultural and pedagogical complexities of innovation diffusion within long-established conventions of mainstream schooling, particularly from the standpoint of students, is wanting. To address this knowledge gap, this thesis inquires into how students evaluate and account for the constraints and affordances of contemporary digital tools when they engage with them as part of their conventional schooling. It documents the attempted integration of a student-led Web 2.0 learning initiative, known as the Student Media Centre (SMC), into the schooling practices of a long-established, high-performing independent senior boys’ school in urban Australia. The study employed an ‘explanatory’ two-phase research design (Creswell, 2003) that combined complementary quantitative and qualitative methods to achieve both breadth of measurement and richness of characterisation. In the initial quantitative phase, a self-reported questionnaire was administered to the senior school student population to determine adoption trends and predictors of SMC usage (N=481). Measurement constructs included individual learning dispositions (learning and performance goals, cognitive playfulness and personal innovativeness), as well as social and technological variables (peer support, perceived usefulness and ease of use). Incremental predictive models of SMC usage were conducted using Classification and Regression Tree (CART) modelling: (i) individual-level predictors, (ii) individual and social predictors, and (iii) individual, social and technological predictors. Peer support emerged as the best predictor of SMC usage. Other salient predictors include perceived ease of use and usefulness, cognitive playfulness and learning goals. On the whole, an overwhelming proportion of students reported low usage levels, low perceived usefulness and a lack of peer support for engaging with the digital learning initiative. The small minority of frequent users reported having high levels of peer support and robust learning goal orientations, rather than being predominantly driven by performance goals. These findings indicate that tensions around social validation, digital learning and academic performance pressures influence students’ engagement with the Web 2.0 learning initiative. The qualitative phase that followed provided insights into these tensions by shifting the analytics from individual attitudes and behaviours to shared social and cultural reasoning practices that explain students’ engagement with the innovation. Six indepth focus groups, comprising 60 students with different levels of SMC usage, were conducted, audio-recorded and transcribed. Textual data were analysed using Membership Categorisation Analysis. Students’ accounts converged around a key proposition. The Web 2.0 learning initiative was useful-in-principle but useless-in-practice. While students endorsed the usefulness of the SMC for enhancing multimodal engagement, extending peer-topeer networks and acquiring real-world skills, they also called attention to a number of constraints that obfuscated the realisation of these design affordances in practice. These constraints were cast in terms of three binary formulations of social and cultural imperatives at play within the school: (i) ‘cool/uncool’, (ii) ‘dominant staff/compliant student’, and (iii) ‘digital learning/academic performance’. The first formulation foregrounds the social stigma of the SMC among peers and its resultant lack of positive network benefits. The second relates to students’ perception of the school culture as authoritarian and punitive with adverse effects on the very student agency required to drive the innovation. The third points to academic performance pressures in a crowded curriculum with tight timelines. Taken together, findings from both phases of the study provide the following key insights. First, students endorsed the learning affordances of contemporary digital tools such as the SMC for enhancing their current schooling practices. For the majority of students, however, these learning affordances were overshadowed by the performative demands of schooling, both social and academic. The student participants saw engagement with the SMC in-school as distinct from, even oppositional to, the conventional social and academic performance indicators of schooling, namely (i) being ‘cool’ (or at least ‘not uncool’), (ii) sufficiently ‘compliant’, and (iii) achieving good academic grades. Their reasoned response therefore, was simply to resist engagement with the digital learning innovation. Second, a small minority of students seemed dispositionally inclined to negotiate the learning affordances and performance constraints of digital learning and traditional schooling more effectively than others. These students were able to engage more frequently and meaningfully with the SMC in school. Their ability to adapt and traverse seemingly incommensurate social and institutional identities and norms is theorised as cultural agility – a dispositional construct that comprises personal innovativeness, cognitive playfulness and learning goals orientation. The logic then is ‘both and’ rather than ‘either or’ for these individuals with a capacity to accommodate both learning and performance in school, whether in terms of digital engagement and academic excellence, or successful brokerage across multiple social identities and institutional affiliations within the school. In sum, this study takes us beyond the familiar terrain of deficit discourses that tend to blame institutional conservatism, lack of resourcing and teacher resistance for low uptake of digital technologies in schools. It does so by providing an empirical base for the development of a ‘third way’ of theorising technological and pedagogical innovation in schools, one which is more informed by students as critical stakeholders and thus more relevant to the lived culture within the school, and its complex relationship to students’ lives outside of school. It is in this relationship that we find an explanation for how these individuals can, at the one time, be digital kids and analogue students.

Sequential nonlinear manifold learning

The computation of compact and meaningful representations of high dimensional sensor data has recently been addressed through the development of Nonlinear Dimensional Reduction (NLDR) algorithms. The numerical implementation of spectral NLDR techniques typically leads to a symmetric eigenvalue problem that is solved by traditional batch eigensolution algorithms. The application of such algorithms in real-time systems necessitates the development of sequential algorithms that perform feature extraction online. This paper presents an efficient online NLDR scheme, Sequential-Isomap, based on incremental singular value decomposition (SVD) and the Isomap method. Example simulations demonstrate the validity and significant potential of this technique in real-time applications such as autonomous systems.

Enabling innovation and facilitating learning in kifs: the role of HRM practices

The aim of this paper is to contribute to our understanding of the link between HR practices, learning orientation and types of innovation in knowledge-intensive firms (KIFs). To this end, we first compiled a comprehensive overview of the existing literature on HR practices aimed at supporting innovation. On the basis of this literature, we then collected and analyzed data from a qualitative study of 19 Danish KIFs recognized for their innovation performance, focusing on links between the HR practices they use to support exploratory and exploitive learning behaviors to enhance incremental and/or radical innovation. The findings from this study demonstrate that KIFs utilize a range of HR practices that enable different learning orientations, based on the type of innovation compatible with their organizational goals.

‘Works well with others’ : examining the different types of small group learning approaches and their implications for law student learning outcomes

In the current regulatory climate, there is increasing expectation that law schools will be able to demonstrate students’ acquisition of learning outcomes regarding collaboration skills. We argue that this is best achieved through a stepped and structured whole-of-curriculum approach to small group learning. ‘Group work’ provides deep learning and opportunities to develop professional skills, but these benefits are not always realised for law students. An issue is that what is meant by ‘group work’ is not always clear, resulting in a learning regime that may not support the attainment of desired outcomes. This paper describes different types of ‘group work', each associated with distinct learning outcomes. It suggests that ‘group work’ as an umbrella term to describe these types is confusing, as it provides little indication to students and teachers of the type of learning that is valued and is expected to take place. ‘Small group learning’ is a preferable general descriptor. Identifying different types of small group learning allows law schools to develop and demonstrate a scaffolded, sequential and incremental approach to fostering law students’ collaboration skills. To support learning and the acquisition of higherorder skills, different types of small group learning are more appropriate at certain stages of the program. This structured approach is consistent with social cognitive theory, which suggests that with the guidance of a supportive teacher, students can develop skills and confidence in one type of activity which then enhances motivation to participate in another.