Semi-supervised learning of statistical models for natural language understanding

Natural language understanding is to specify a computational model that maps sentences to their semantic mean representation. In this paper, we propose a novel framework to train the statistical models without using expensive fully annotated data. In particular, the input of our framework is a set of sentences labeled with abstract semantic annotations. These annotations encode the underlying embedded semantic structural relations without explicit word/semantic tag alignment. The proposed framework can automatically induce derivation rules that map sentences to their semantic meaning representations. The learning framework is applied on two statistical models, the conditional random fields (CRFs) and the hidden Markov support vector machines (HM-SVMs). Our experimental results on the DARPA communicator data show that both CRFs and HM-SVMs outperform the baseline approach, previously proposed hidden vector state (HVS) model which is also trained on abstract semantic annotations. In addition, the proposed framework shows superior performance than two other baseline approaches, a hybrid framework combining HVS and HM-SVMs and discriminative training of HVS, with a relative error reduction rate of about 25% and 15% being achieved in F-measure.

Munkahelyi tanulást befolyásoló tényezők – humán-számítógép együttműködés vizsgálata (Influencing factors of on-the-job learning – Analysis of human-computer collaboration)

A komplex, dinamikus, tudásalapú társadalomban nemcsak a tanulás formái, hanem a tanulás helyszínei is módosulnak, s a munkahely tanulásban betöltött szerepe felértékelődött. A munkahelyi környezet is számos átalakuláson esett át, az információs és kommunikációs technológiák (IKT) fejlődésével egyidejűleg lehetővé vált többek között a távmunka, jelentősen átformálva a munkavégzés és a munkahelyi interakciók módját. A kutatók arra keresték a választ kutatásukban, hogy a szervezeten belül milyen tényezők támogatják vagy gátolják a munkahelyi tanulást. A kutatás fő üzenete, hogy a tanulás keretrendszere, az egyéni képességek és az észlelt tanulási szituáció együttesen határozza meg a munkahelyi tanulást. A kutatók eredményüket kvalitatív kutatással feltárt három esettanulmányon keresztül ismertetik. ____ In a knowledge-based society not only the forms of learning have been changed but also the places of learning. The role of workplace in the learning process is becoming more important. Meantime, there has been a substantial change in the working conditions as the development in information and communication technologies (ICTs) makes it possible to telecommute transforming remarkably the way of working and the interactions at the workplace. The central question of the research is which intra-organizational factors support or hinder onthe- job learning. The main message of the research is that the learning framework, the individual cognitive competences and the perceived learning situation influence collectively on-the-job learning. Authors present the results of the qualitative research though three case studies.

A multimedia indexing and retrieval framework for multimedia database systems

The main challenges of multimedia data retrieval lie in the effective mapping between low-level features and high-level concepts, and in the individual users' subjective perceptions of multimedia content. ^ The objectives of this dissertation are to develop an integrated multimedia indexing and retrieval framework with the aim to bridge the gap between semantic concepts and low-level features. To achieve this goal, a set of core techniques have been developed, including image segmentation, content-based image retrieval, object tracking, video indexing, and video event detection. These core techniques are integrated in a systematic way to enable the semantic search for images/videos, and can be tailored to solve the problems in other multimedia related domains. In image retrieval, two new methods of bridging the semantic gap are proposed: (1) for general content-based image retrieval, a stochastic mechanism is utilized to enable the long-term learning of high-level concepts from a set of training data, such as user access frequencies and access patterns of images. (2) In addition to whole-image retrieval, a novel multiple instance learning framework is proposed for object-based image retrieval, by which a user is allowed to more effectively search for images that contain multiple objects of interest. An enhanced image segmentation algorithm is developed to extract the object information from images. This segmentation algorithm is further used in video indexing and retrieval, by which a robust video shot/scene segmentation method is developed based on low-level visual feature comparison, object tracking, and audio analysis. Based on shot boundaries, a novel data mining framework is further proposed to detect events in soccer videos, while fully utilizing the multi-modality features and object information obtained through video shot/scene detection. ^ Another contribution of this dissertation is the potential of the above techniques to be tailored and applied to other multimedia applications. This is demonstrated by their utilization in traffic video surveillance applications. The enhanced image segmentation algorithm, coupled with an adaptive background learning algorithm, improves the performance of vehicle identification. A sophisticated object tracking algorithm is proposed to track individual vehicles, while the spatial and temporal relationships of vehicle objects are modeled by an abstract semantic model. ^

Essays in Empirical Operations Management: Bayesian Learning of Service Quality and Structural Estimation of Complementary Product Pricing and Inventory Management

This dissertation contributes to the rapidly growing empirical research area in the field of operations management. It contains two essays, tackling two different sets of operations management questions which are motivated by and built on field data sets from two very different industries --- air cargo logistics and retailing.

The first essay, based on the data set obtained from a world leading third-party logistics company, develops a novel and general Bayesian hierarchical learning framework for estimating customers' spillover learning, that is, customers' learning about the quality of a service (or product) from their previous experiences with similar yet not identical services. We then apply our model to the data set to study how customers' experiences from shipping on a particular route affect their future decisions about shipping not only on that route, but also on other routes serviced by the same logistics company. We find that customers indeed borrow experiences from similar but different services to update their quality beliefs that determine future purchase decisions. Also, service quality beliefs have a significant impact on their future purchasing decisions. Moreover, customers are risk averse; they are averse to not only experience variability but also belief uncertainty (i.e., customer's uncertainty about their beliefs). Finally, belief uncertainty affects customers' utilities more compared to experience variability.

The second essay is based on a data set obtained from a large Chinese supermarket chain, which contains sales as well as both wholesale and retail prices of un-packaged perishable vegetables. Recognizing the special characteristics of this particularly product category, we develop a structural estimation model in a discrete-continuous choice model framework. Building on this framework, we then study an optimization model for joint pricing and inventory management strategies of multiple products, which aims at improving the company's profit from direct sales and at the same time reducing food waste and thus improving social welfare.

Collectively, the studies in this dissertation provide useful modeling ideas, decision tools, insights, and guidance for firms to utilize vast sales and operations data to devise more effective business strategies.

Bayesian Learning with Dependency Structures via Latent Factors, Mixtures, and Copulas

Bayesian methods offer a flexible and convenient probabilistic learning framework to extract interpretable knowledge from complex and structured data. Such methods can characterize dependencies among multiple levels of hidden variables and share statistical strength across heterogeneous sources. In the first part of this dissertation, we develop two dependent variational inference methods for full posterior approximation in non-conjugate Bayesian models through hierarchical mixture- and copula-based variational proposals, respectively. The proposed methods move beyond the widely used factorized approximation to the posterior and provide generic applicability to a broad class of probabilistic models with minimal model-specific derivations. In the second part of this dissertation, we design probabilistic graphical models to accommodate multimodal data, describe dynamical behaviors and account for task heterogeneity. In particular, the sparse latent factor model is able to reveal common low-dimensional structures from high-dimensional data. We demonstrate the effectiveness of the proposed statistical learning methods on both synthetic and real-world data.

HRM and Innovation: A Multi-level Organizational Learning Perspective

Drawing on the 4I organizational learning framework (Crossan et al., 1999), this article develops a model to explain the multi-level and cross-level relationships between HRM practices and innovation. Individual, team, and organizational level learning stocks are theorized to explain how HRM practices affect innovation at a given level. Feed-forward and feedback learning flows explain how cross-level effects of HRM practices on innovation take place. In addition, we propose that HRM practices fostering individual, team, and organizational level learning should form a coherent system to facilitate the emergence of innovation. The article is concluded with discussions on its contributions and potential future research directions.

What do clients learn from counseling?

Thirty-five clients who received counseling participated in this exploratory study by completing a letter to a friend that described in as much detail as possible what they had learned from counseling. The participants’ written responses were analyzed using a content analysis approach. The analysis indicated that the data were best categorized in terms of three broad areas of learnings (Self, Relations with Others, and the Process of Learning and Change). The Self taxonomy was found to consist of six hierarchical levels. The Relations with Others taxonomy consisted of five hierarchical levels, while the Process of Learning and Change taxonomy consisted of five hierarchical levels. The results suggested that these three taxonomies offer a promising and exciting way to view the impact of counseling within a learning framework. If these taxonomies are found to be stable in future research and clients are easily classified using the taxonomies then this approach may have implications for counseling. It may well be that to maximise the learnings counselors could use specific strategies and techniques to enhance their clients’ learning in the three areas.

Tracking rail safety : incident and accident investigation in Australia

Scoping Project: Currently no national or structured learning framework available in Aus or NZ Current Project: Develop a national training program & capability framework for rail incident investigators - Establish the potential market demand - Define the curricula for a multi-level national training program - Explore training providers & delivery options

Stars are made of glass : children as capable and creative communicators

This booklet is the third in the Research in Practice Series, designed to complement Belonging, being & becoming: The Early Years Learning Framework for Australia (DEEWR, 2009). It focuses on Learning Outcome 5 of the Early Years Learning Framework (EYLF): Children are effective communicators (DEEWR, 2009).

Australian ePortfolio Project–Stage 2 ePortfolio use by university students in Australia : Developing a sustainable community of practice Case studies extracted from the final project report December 2009

Fourteen sase studies extracted from the final project report - December 2009 Australian Flexible Learning Framework: E-portfolios Community of Practice (Aus) Personal learning plans and ePortfolio (Aus) RMIT University: Introducing ePortfolios (Aus) ePortfolio Practice: ALTC Exchange (Aus) Australian PebblePad User Group (APpUG) (Aus) ePortfolios in the library and information services sector (Aus) PDP and ePortfolios UK (UK) SURF NL Portfolio (Netherlands) University of Canterbury ePortfolio (NZ) AAEEBL: Association for Authentic, Experiential and Evidence-Based Learning (USA) Midlands Eportfolio Group, West Midlands(UK) EPAC: Electronic Portfolio Action and Communication (USA) Scottish Higher Education PDP Forum (UK) Centre for Recording Achievement (CRA)(UK)

Evidence based practice

An integral part of teaching and a principle underpinning professional practice in the early years is the importance of reflecting on and researching our own practice. For example, in Australia, the Early Years Learning Framework: Belonging, Being and Becoming identifies “ongoing learning and reflective practice” (DEEWR, 2009, p. 13) as one of the five principles distilled from theories and research evidence that underpin professional practice in the early years. Recognising teaching as encompassing the role of researching pedagogical practice highlights that teaching is not simply practical or procedural but requires intellectual work. This chapter details evidence based practice (EBP) in early years education and highlights four questions: 1. What is evidence based practice?; 2. What evidence do I draw on?; 3. How might I discern relevant evidence?; and 4. What is my part in generating research evidence?

Still valuing play

Play has had a prominent position in early childhood education and care (ECEC) for over 200 years. As educators, we tend to talk about young children learning through play as a matter of fact. In our first national Early Years Learning Framework (Department of Education, Employment and Workplace Relations, 2009), play is promoted as the right of all children, an integral part of being a child and as the prime context for learning in the early years. While the Early Years Learning Framework (EYLF) defines its use of the term ‘play’, there are differing perspectives on what constitutes play, the relationship between play and learning,and the educator’s role in play. In this context, it might be interesting to go a little deeper, and to look at some different perspectives on play and learning.

Get real : the development, implementation and evaluation of an intervention for primary aged children with High Functioning Autism

Children with Autism Spectrum Disorder experience difficulty in communication and in understanding the social world which can have negative consequences for their relationships, in managing emotions, and generally dealing with the challenges of everyday life. This thesis examines the effectiveness of the Active and Reflective components of the Get REAL program through the assessment of the detailed coding of video-recorded observations and longitudinal quantitative analysis. The aim of Get REAL is to increase the social, emotional, and cognitive learning of children with High Functioning Autism (HFA). Get REAL is a group program designed specifically for use in inclusive primary school settings. The Get REAL program was designed in response to the mixed success of generalisation of learning to new contexts of existing social skills programs. The theoretical foundation of Get REAL is based upon pedagogical theory and learning theory to facilitate transfer of learning, combined with experiential, individualised, evaluative and organisational approaches. This thesis is by publication and consists of four refereed journal papers; 1 accepted for publication and 3 that are under review. Paper 1 describes the development and theoretical basis of the Get REAL program and provides detail of the program structure and learning cycle. The focus of Paper 1 reflects the first question of interest in the thesis which is about the extent to which learning derived from participation in the program can be generalised to other contexts. Participants are 16 children with HFA ranging in age from 8-13 years. Results provided support for the generalisability of learning from Get REAL to home and school evidenced by parent and teacher data collected pre and post participation in Get REAL. Following establishment of the generalisation of learning from Get REAL, Papers 2 and 3 focus on the Active and Reflective components of the program in order to examine how individual and group learning takes place. Participants (N = 12) in the program are video-taped during the Active and Reflective Sessions. Using identical coding protocols of video data, improvements in prosocial behaviour and diminishing of inappropriate behaviours were apparent with the exception of perspective taking. Data also revealed that 2 of the participants had atypical trajectories. An in-depth case study analysis was then conducted with these 2 participants in Paper 4. Data included reports from health care and education professionals within the school and externally (e.g., paediatrician) and identified the multi-faceted nature of care needed for children with comorbid diagnoses and extremely challenging family circumstances as a complex task to effect change. Results of this research support the effectiveness of the Get REAL program in promoting pro social behaviours such as improvements in engaging with others and emotional regulation, and in diminishing unwanted behaviours such as conduct problems. Further, the gains made by the participating children were found to be generalisable beyond Get REAL to home and other school settings. The research contained in the thesis adds to current knowledge about how learning can take place for children with HFA. Results show that an experiential learning framework with a focus on social cognition, together with explicit teaching, scaffolded with video feedback, are key ingredients for the generalisation of social learning to broader contexts.

Transformative approaches to sustainability

How to live sustainably is a topic of local, national and international importance. The Australian National Curriculum (ACARA, 2011) identifies sustainability as a cross-disciplinary strand, obligating teachers to build sustainability into their pedagogical practices. In early childhood education, the Early Years Learning Framework (2009) and more recently, the National Quality Framework (2011) provide impetus for early childhood education for sustainably (ECEfS). This article discusses ECEfS, but first, it addresses climate change putting this into a sustainability perspective.

Automatic classification of human epithelial type 2 cell indirect immunofluorescence images using cell pyramid matching

This paper describes a novel system for automatic classification of images obtained from Anti-Nuclear Antibody (ANA) pathology tests on Human Epithelial type 2 (HEp-2) cells using the Indirect Immunofluorescence (IIF) protocol. The IIF protocol on HEp-2 cells has been the hallmark method to identify the presence of ANAs, due to its high sensitivity and the large range of antigens that can be detected. However, it suffers from numerous shortcomings, such as being subjective as well as time and labour intensive. Computer Aided Diagnostic (CAD) systems have been developed to address these problems, which automatically classify a HEp-2 cell image into one of its known patterns (eg. speckled, homogeneous). Most of the existing CAD systems use handpicked features to represent a HEp-2 cell image, which may only work in limited scenarios. We propose a novel automatic cell image classification method termed Cell Pyramid Matching (CPM), which is comprised of regional histograms of visual words coupled with the Multiple Kernel Learning framework. We present a study of several variations of generating histograms and show the efficacy of the system on two publicly available datasets: the ICPR HEp-2 cell classification contest dataset and the SNPHEp-2 dataset.