Supplementary information : weighted tree kernels for sequence analysis

Genomic sequences are fundamentally text documents, admitting various representations according to need and tokenization. Gene expression depends crucially on binding of enzymes to the DNA sequence at small, poorly conserved binding sites, limiting the utility of standard pattern search. However, one may exploit the regular syntactic structure of the enzyme's component proteins and the corresponding binding sites, framing the problem as one of detecting grammatically correct genomic phrases. In this paper we propose new kernels based on weighted tree structures, traversing the paths within them to capture the features which underpin the task. Experimentally, we and that these kernels provide performance comparable with state of the art approaches for this problem, while offering significant computational advantages over earlier methods. The methods proposed may be applied to a broad range of sequence or tree-structured data in molecular biology and other domains.

Strengthening Social and Emotional Learning in Children with Special Needs

Locally and globally, guiding children’s social and emotional development is no longer optional for educators. Research undertaken over the last 20 years provides compelling evidence that early and ongoing development of socio-emotional skills contributes to an individual’s overall health, wellbeing and competence throughout life. Moreover, competence in this domain is now recognised as fundamental to school readiness, school adjustment and academic achievement. As a consequence, social and emotional learning (SEL) is an important theme in current educational policy, curriculum frameworks and classroom practice. This chapter focuses on a particular group of vulnerable learners – children with special needs – and highlights key strategies for educators to use in their everyday classroom practices to strengthen SEL in children from early years through to the end of primary school.

Managing and learning with multiple models: Objectives and optimization algorithms

The quality of environmental decisions should be gauged according to managers' objectives. Management objectives generally seek to maximize quantifiable measures of system benefit, for instance population growth rate. Reaching these goals often requires a certain degree of learning about the system. Learning can occur by using management action in combination with a monitoring system. Furthermore, actions can be chosen strategically to obtain specific kinds of information. Formal decision making tools can choose actions to favor such learning in two ways: implicitly via the optimization algorithm that is used when there is a management objective (for instance, when using adaptive management), or explicitly by quantifying knowledge and using it as the fundamental project objective, an approach new to conservation.This paper outlines three conservation project objectives - a pure management objective, a pure learning objective, and an objective that is a weighted mixture of these two. We use eight optimization algorithms to choose actions that meet project objectives and illustrate them in a simulated conservation project. The algorithms provide a taxonomy of decision making tools in conservation management when there is uncertainty surrounding competing models of system function. The algorithms build upon each other such that their differences are highlighted and practitioners may see where their decision making tools can be improved. © 2010 Elsevier Ltd.

Exploring with Grammar in the Primary Years: Learning about Language in the Australian Curriculum: English

This new volume, Exploring with Grammar in the Primary Years (Exley, Kevin & Mantei, 2014), follows on from Playing with Grammar in the Early Years (Exley & Kervin, 2013). We extend our thanks to the ALEA membership for their take up of the first volume and the vibrant conversations around our first attempt at developing a pedagogy for the teaching of grammar in the early years. Your engagement at locally held ALEA events has motivated us to complete this second volume and reassert our interest in the pursuit of socially-just outcomes in the primary years. As noted in Exley and Kervin (2013), we believe that mastering a range of literacy competences includes not only the technical skills for learning, but also the resources for viewing and constructing the world (Freire and Macdeo, 1987). Rather than seeing knowledge about language as the accumulation of technical skills alone, the viewpoint to which we subscribe treats knowledge about language as a dialectic that evolves from, is situated in, and contributes to active participation within a social arena (Halliday, 1978). We acknowledge that to explore is to engage in processes of discovery as we look closely and examine the opportunities before us. As such, we draw on Janks’ (2000; 2014) critical literacy theory to underpin many of the learning experiences in this text. Janks (2000) argues that effective participation in society requires knowledge about how the power of language promotes views, beliefs and values of certain groups to the exclusion of others. Powerful language users can identify not only how readers are positioned by these views, but also the ways these views are conveyed through the design of the text, that is, the combination of vocabulary, syntax, image, movement and sound. Similarly, powerful designers of texts can make careful modal choices in written and visual design to promote certain perspectives that position readers and viewers in new ways to consider more diverse points of view. As the title of our text suggests, our activities are designed to support learners in exploring the design of texts to achieve certain purposes and to consider the potential for the sharing of their own views through text production. In Exploring with Grammar in the Primary Years, we focus on the Year 3 to Year 6 grouping in line with the Australian Curriculum, Assessment and Reporting Authority’s (hereafter ACARA) advice on the ‘nature of learners’ (ACARA, 2014). Our goal in this publication is to provide a range of highly practical strategies for scaffolding students’ learning through some of the Content Descriptions from the Australian Curriculum: English Version 7.2, hereafter AC:E (ACARA, 2014). We continue to express our belief in the power of using whole texts from a range of authentic sources including high quality children’s literature, the internet, and examples of community-based texts to expose students to the richness of language. Taking time to look at language patterns within actual texts is a pathway to ‘…capture interest, stir the imagination and absorb the [child]’ into the world of language and literacy (Saxby, 1993, p. 55). It is our intention to be more overt this time and send a stronger message that our learning experiences are simply ‘sample’ activities rather than a teachers’ workbook or a program of study to be followed. We’re hoping that teachers and students will continue to explore their bookshelves, the internet and their community for texts that provide powerful opportunities to engage with language-based learning experiences. In the following three sections, we have tried to remain faithful to our interpretation of the AC:E Content Descriptions without giving an exhaustive explanation of the grammatical terms. This recently released curriculum offers a new theoretical approach to building students’ knowledge about language. The AC:E uses selected traditional terms through an approach developed in systemic functional linguistics (see Halliday and Matthiessen, 2004) to highlight the dynamic forms and functions of multimodal language in texts. For example, the following statement, taken from the ‘Language: Knowing about the English language’ strand states: English uses standard grammatical terminology within a contextual framework, in which language choices are seen to vary according to the topics at hand, the nature and proximity of the relationships between the language users, and the modalities or channels of communication available (ACARA, 2014). Put simply, traditional grammar terms are used within a functional framework made up of field, tenor, and mode. An understanding of genre is noted with the reference to a ‘contextual framework’. The ‘topics at hand’ concern the field or subject matter of the text. The ‘relationships between the language users’ is a description of tenor. There is reference to ‘modalities’, such as spoken, written or visual text. We posit that this innovative approach is necessary for working with contemporary multimodal and cross-cultural texts (see Exley & Mills, 2012). Other excellent tomes, such as Derewianka (2011), Humphrey, Droga and Feez (2012), and Rossbridge and Rushton (2011) provide more comprehensive explanations of this unique metalanguage, as does the AC:E Glossary. We’ve reproduced some of the AC:E Glossary at the end of this publication. We’ve also kept the same layout for our learning experiences, ensuring that our teacher notes are not only succinct but also prudent in their placement. Each learning experience is connected to a Content Description from the AC:E and contains an experience with an identified purpose, suggested resource text and a possible sequence for the experience that always commences with an orientation to text followed by an examination of a particular grammatical resource. Our plans allow for focused discussion, shared exploration and opportunities to revisit the same text for the purpose of enhancing meaning making. Some learning experiences finish with deconstruction of a stimulus text while others invite students to engage in the design of new texts. We encourage you to look for opportunities in your own classrooms to move from text deconstruction to text design. In this way, students can express not only their emerging grammatical understandings, but also the ways they might position readers or viewers through the creation of their own texts. We expect that each of these learning experiences will vary in the time taken. Some may indeed take a couple if not a few teaching episodes to work through, especially if students are meeting a concept or a pedagogical strategy for the first time. We hope you use as much, or as little, of each experience as is needed for your students. We do not want the teaching of grammar to slip into a crisis of irrelevance or to be seen as a series of worksheet drills with finite answers. We firmly believe that strategies for effective deconstruction and design practice, however, have much portability. We three are very keen to hear from teachers who are adopting and adapting these learning experiences in their classrooms. Please email us on b.exley@qut.edu.au, lkervin@uow.edu.au or jessicam@ouw.edu.au. We’d love to continue the conversation with you over time. Beryl Exley, Lisa Kervin & Jessica Mantei

Learning challenges of NGOs in development : Co-operation of Finnish NGOs in Morogoro, Tanzania

Non-governmental organisations (NGOs) have gained an important role in development co-operation during the last two decades. The development funding channelled through NGOs has increased and the number of NGOs engaged in development activities, both North and South, has been growing. Supporting NGOs has been seen as one way to strengthen civil society in the South and to provide potential for enhancing more effective development than the state, and to exercise participatory development and partnership in their North-South relationships. This study focuses on learning in the co-operation practices of small Finnish NGOs in Morogoro, Tanzania. Drawing on the cultural-historical activity theory and the theory of expansive learning, in this study I understand learning as a qualitative change in the actual co-operation practices. The qualitative change, for its part, emerges out of attempts to deal with the contradictions in the present activity. I use the concept of developmental contradiction in exploring the co-operation of the small Finnish NGOs with their Tanzanian counterparts. Developmental contradiction connects learning to actual practice and its historical development. By history, in this study I refer to multiple developmental trajectories, such as trajectories of individual participants, organisations, co-operation practices and the institutional system in which the NGO-development co-operation is embedded. In the empirical chapters I explore the co-operation both in the development co-operation projects and in micro-level interaction between partners taking place within the projects. I analyse the perceptions of the Finnish participants about the different developmental trajectories, the tensions, inclusions and exclusions in the evolving object of co-operation in one project, the construction of power relations in project meetings in three projects, and the collision of explicated partnership with the emerging practice of trusteeship in one project. On the basis of the empirical analyses I elaborate four developmental contradictions and learning challenges for the co-operation. The developmental contradictions include: 1) implementing a ready-made Finnish project idea vs. taking the current activities of Tanzanian NGO as a starting point; 2) gaining experiences and cultural interaction vs. access to outside funding; 3) promoting the official tools of development co-operation in training vs. use of tools and procedures taken from the prior activities of both partners in actual practice; and 4) asymmetric relations between the partners vs. rhetoric of equal partnership. Consequently, on the basis of developmental contradictions four learning challenges are suggested: a shift from legitimation of Finnish ideas to negotiation, transcending the separate objects and finding a partly joint object, developing locally shared tools for the co-operation, and identification and reflection of the power relations in the practice of co-operation. Keywords: activity theory; expansive learning; NGO development co-operation; partnership; power

Transcending “the Impossible” : From Dead End to Expansive Learning

This dissertation investigates changes in bank work and the experience of impossibility attached to these by workers at the local level from the viewpoint of work-related well-being and collective learning. A special challenge in my work is to conceptualize the experience of impossibility as related to change, and as a starting point and tool for development work. The subject of the dissertation, solving the impossible as a collective learning process, came up as a central theme in an earlier project: Work Units between the Old and the New (1997 – 1999). Its aim was to investigate how change is constructed as a long-term process, starting from the planning of the change until its final realization in everyday banking work. I studied changes taking place in the former Postipankki (Postal Bank), later called Leonia. The three-year study involved the Branch Office of Martinlaakso, and was conducted from the perspective of well-being in a change process. The sense of impossibility involved in changes turned out to be one of the most crucial factors impairing the sense of well-being. The work community that was the target of my study did not have the available tools to construct the change locally, or to deal with the change-related impossibility by solving it through a mutual process among themselves. During the last year of the project, I carried out an intervention for development in the Branch Office, as collaboration between the researchers and the workers. The purpose of the intervention was to resolve such perceived change-related impossibility as experienced repeatedly and considered by the work community as relevant to work-related well-being. The documentation of the intervention – audio records from development sessions, written assignments by workers and assessment or evaluation interviews – constitute the essential data for my dissertation. The earlier data, collected and analysed during the first two years, provides a historical perspective on the process, all the way from construction of the impossibility towards resolving and transcending it. The aim of my dissertation is to understand the progress of developmental intervention as a shared, possibly expansive learning process within a work community and thus to provide tools for perceiving and constructing local change. I chose the change-related impossibility as a starting point for development work in the work community and as a target of conceptualization. This, I feel, is the most important contribution of my dissertation. While the intervention was in progress, the concept of impossibility started emerging as a stimulating tool for development work. An understanding of such a process can be applied to development work outside banking work as well. According to my results, it is pivotal that a concept stimulating development is strongly connected with everyday experiences of and speech about changes in work activity, as well as with the theoretical framework of work development. During this process, development work on a local level became of utmost interest as a case study for managing change. Theoretically, this was conceptualized as so-called second-order work and this concept accompanies us all the way through the research process. Learning second-order work and constructing tools based on this work have proved crucial for promoting well-being in the change circumstances in a local work unit. The lack of second-order work has led to non-well-being and inability to transcend the change-related sense of impossibility in the work community. Solving the impossible, either individually or situationally, did not orient the workers towards solving problems of impossibility together as a work community. Because the experience of the impossibility and coming to terms with transcending it are the starting point and the target of conceptualization in this dissertation, the research provides a fresh viewpoint on the theoretical framework of change and developmental work. My dissertation can facilitate construction of local changes necessitated by the recent financial crisis, and thus promote fluency and well-being in work units. It can also support change-related well-being in other areas of working life.

Feature selection for unsupervised learning

In this paper, we present a methodology for identifying best features from a large feature space. In high dimensional feature space nearest neighbor search is meaningless. In this feature space we see quality and performance issue with nearest neighbor search. Many data mining algorithms use nearest neighbor search. So instead of doing nearest neighbor search using all the features we need to select relevant features. We propose feature selection using Non-negative Matrix Factorization(NMF) and its application to nearest neighbor search. Recent clustering algorithm based on Locally Consistent Concept Factorization(LCCF) shows better quality of document clustering by using local geometrical and discriminating structure of the data. By using our feature selection method we have shown further improvement of performance in the clustering.

A Split-and-Merge Dictionary Learning Algorithm for Sparse Representation: Application to Image Denoising

In big data image/video analytics, we encounter the problem of learning an over-complete dictionary for sparse representation from a large training dataset, which cannot be processed at once because of storage and computational constraints. To tackle the problem of dictionary learning in such scenarios, we propose an algorithm that exploits the inherent clustered structure of the training data and make use of a divide-and-conquer approach. The fundamental idea behind the algorithm is to partition the training dataset into smaller clusters, and learn local dictionaries for each cluster. Subsequently, the local dictionaries are merged to form a global dictionary. Merging is done by solving another dictionary learning problem on the atoms of the locally trained dictionaries. This algorithm is referred to as the split-and-merge algorithm. We show that the proposed algorithm is efficient in its usage of memory and computational complexity, and performs on par with the standard learning strategy, which operates on the entire data at a time. As an application, we consider the problem of image denoising. We present a comparative analysis of our algorithm with the standard learning techniques that use the entire database at a time, in terms of training and denoising performance. We observe that the split-and-merge algorithm results in a remarkable reduction of training time, without significantly affecting the denoising performance.

Contributions to learning Bayesian network models from weakly supervised data: Application to Assisted Reproductive Technologies and Software Defect Classification

162 p.

Learning Multirobot Hose Transportation and Deployment by Distributed Round-Robin Q-Learning

Multi-Agent Reinforcement Learning (MARL) algorithms face two main difficulties: the curse of dimensionality, and environment non-stationarity due to the independent learning processes carried out by the agents concurrently. In this paper we formalize and prove the convergence of a Distributed Round Robin Q-learning (D-RR-QL) algorithm for cooperative systems. The computational complexity of this algorithm increases linearly with the number of agents. Moreover, it eliminates environment non sta tionarity by carrying a round-robin scheduling of the action selection and execution. That this learning scheme allows the implementation of Modular State-Action Vetoes (MSAV) in cooperative multi-agent systems, which speeds up learning convergence in over-constrained systems by vetoing state-action pairs which lead to undesired termination states (UTS) in the relevant state-action subspace. Each agent's local state-action value function learning is an independent process, including the MSAV policies. Coordination of locally optimal policies to obtain the global optimal joint policy is achieved by a greedy selection procedure using message passing. We show that D-RR-QL improves over state-of-the-art approaches, such as Distributed Q-Learning, Team Q-Learning and Coordinated Reinforcement Learning in a paradigmatic Linked Multi-Component Robotic System (L-MCRS) control problem: the hose transportation task. L-MCRS are over-constrained systems with many UTS induced by the interaction of the passive linking element and the active mobile robots.

On-line learning of mutually orthogonal subspaces for face recognition by image sets.

We address the problem of face recognition by matching image sets. Each set of face images is represented by a subspace (or linear manifold) and recognition is carried out by subspace-to-subspace matching. In this paper, 1) a new discriminative method that maximises orthogonality between subspaces is proposed. The method improves the discrimination power of the subspace angle based face recognition method by maximizing the angles between different classes. 2) We propose a method for on-line updating the discriminative subspaces as a mechanism for continuously improving recognition accuracy. 3) A further enhancement called locally orthogonal subspace method is presented to maximise the orthogonality between competing classes. Experiments using 700 face image sets have shown that the proposed method outperforms relevant prior art and effectively boosts its accuracy by online learning. It is shown that the method for online learning delivers the same solution as the batch computation at far lower computational cost and the locally orthogonal method exhibits improved accuracy. We also demonstrate the merit of the proposed face recognition method on portal scenarios of multiple biometric grand challenge.

Determining of the delay time for a heating ventilating and air-conditioning plant using two weighted neural network approach

This paper presents an two weighted neural network approach to determine the delay time for a heating, ventilating and air-conditioning (HVAC) plan to respond to control actions. The two weighted neural network is a fully connected four-layer network. An acceleration technique was used to improve the General Delta Rule for the learning process. Experimental data for heating and cooling modes were used with both the two weighted neural network and a traditional mathematical method to determine the delay time. The results show that two weighted neural networks can be used effectively determining the delay time for AVAC systems.

Continuous speech recognition based on ICA and geometrical learning

We investigate the use of independent component analysis (ICA) for speech feature extraction in digits speech recognition systems. We observe that this may be true for recognition tasks based on Geometrical Learning with little training data. In contrast to image processing, phase information is not essential for digits speech recognition. We therefore propose a new scheme that shows how the phase sensitivity can be removed by using an analytical description of the ICA-adapted basis functions. Furthermore, since the basis functions are not shift invariant, we extend the method to include a frequency-based ICA stage that removes redundant time shift information. The digits speech recognition results show promising accuracy. Experiments show that the method based on ICA and Geometrical Learning outperforms HMM in a different number of training samples.

Research on multi-degree-of-freedom neurons with weighted graphs

In this paper, we redefine the sample points set in the feature space from the point of view of weighted graph and propose a new covering model - Multi-Degree-of-Freedorn Neurons (MDFN). Base on this model, we describe a geometric learning algorithm with 3-degree-of-freedom neurons. It identifies the sample points secs topological character in the feature space, which is different from the traditional "separation" method. Experiment results demonstrates the general superiority of this algorithm over the traditional PCA+NN algorithm in terms of efficiency and accuracy.

Continuous speech recognition based on ICA and geometrical learning

We investigate the use of independent component analysis (ICA) for speech feature extraction in digits speech recognition systems. We observe that this may be true for recognition tasks based on Geometrical Learning with little training data. In contrast to image processing, phase information is not essential for digits speech recognition. We therefore propose a new scheme that shows how the phase sensitivity can be removed by using an analytical description of the ICA-adapted basis functions. Furthermore, since the basis functions are not shift invariant, we extend the method to include a frequency-based ICA stage that removes redundant time shift information. The digits speech recognition results show promising accuracy. Experiments show that the method based on ICA and Geometrical Learning outperforms HMM in a different number of training samples.