Preference Networks: probabilistic models for recommendation systems

Abstract
Recommender systems are important to help users select relevant and personalised information over massive amounts of data available. We propose an unified framework called Preference Network (PN) that jointly models various types of domain knowledge for the task of recommendation. The PN is a probabilistic model that systematically combines both content-based filtering and collaborative filtering into a single conditional
Markov random field. Once estimated, it serves as a probabilistic database that supports various useful queries such as rating prediction and top-N recommendation. To handle the challenging problem of learning large networks of users and items, we employ a simple but effective pseudo-likelihood with regularisation. Experiments on the movie rating data demonstrate the merits of the PN.

Towards designing an email classification system using multi-view based semi-supervised learning

The goal of email classification is to classify user emails into spam and legitimate ones. Many supervised learning algorithms have been invented in this domain to accomplish the task, and these algorithms require a large number of labeled training data. However, data labeling is a labor intensive task and requires in-depth domain knowledge. Thus, only a very small proportion of the data can be labeled in practice. This bottleneck greatly degrades the effectiveness of supervised email classification systems. In order to address this problem, in this work, we first identify some critical issues regarding supervised machine learning-based email classification. Then we propose an effective classification model based on multi-view disagreement-based semi-supervised learning. The motivation behind the attempt of using multi-view and semi-supervised learning is that multi-view can provide richer information for classification, which is often ignored by literature, and semi-supervised learning supplies with the capability of coping with labeled and unlabeled data. In the evaluation, we demonstrate that the multi-view data can improve the email classification than using a single view data, and that the proposed model working with our algorithm can achieve better performance as compared to the existing similar algorithms.

Critical success factors in e‐learning ecosystems: a qualitative study

Abstract
Purpose – The purpose of this paper is to evaluate the critical success factors for sustainable e-learning in an e-learning ecosystem framework. Three critical components of the e-learning ecosystem including principles and methods, processes and systems, and substance and content are considered based on a comprehensive review of the relevant literature in e-learning.
Design/methodology/approach – Systematic interviews are conducted with experts in e-learning for identifying the critical success factors to sustainable e-learning within an e-learning ecosystem framework. This leads to the development of an e-learning success model that describes the underlying relationship between and among the identified critical success factors.
Findings – A comprehensive analysis of the interview results shows that there are several barriers to the effective adoption of the proposed e-learning success model for improving the effectiveness of e- learning. These barriers include a lack of understanding of the technologies behind various pedagogies, insufficiencies of the popular learning management systems, and the sustainability of the learning objects repositories.
Research limitations/implications – The paper highlights the criticality of synergizing the three components of e-learning ecosystems namely pedagogies, technologies and management of learning resources for achieving a sustainable e-learning success.
Practical implications – A better understanding of these barriers would help e-learning stakeholders develop appropriate strategies and policies for the implementation of the proposed e-learning success model towards creating a sustainable e-learning environment.
Originality/value – Specific contributions of this research to the entire e-learning community are discussed with recommendations for concerted policy measures to eliminate the identified barriers in the process of adopting the developed e-learning success model.

E-learning opportunities and challenges for legal education in rural Victoria

Access to justice extends beyond consideration of the systems and institutions of justice; it includes infrastructure such as transport, health, education and communications. Rural, regional and remote (‘RRR’) communities are more likely to face difficulties in accessing advice and accurate information on laws and processes available for resolution of disputes. Perhaps more fundamentally, they rarely have a voice in effecting reforms in laws and related policies. For several decades, community legal centres, legal aid, courts, and a range of other institutions have used community legal education programs to improve knowledge and access to law and justice systems, services and organisations. The recent Productivity Commission Inquiry into Access to Justice Arrangements notes that, ‘Better coordination and greater quality control in the development and delivery of these [community legal education, legal information] services would improve their value and reach.’ At the same time, research into the professional needs of RRR legal practitioners has found that many of these practitioners face considerable difficulties accessing good quality continuing professional development (‘CPD’) and informal networking/support opportunities.6 Current and emerging internet-based technologies open up opportunities for legal organisations to better meet the educational needs of both rural communities and legal practitioners. Though limitations still exist at multiple levels, relatively low-cost, media-rich, synchronous and tailored education programs can now be delivered effectively in many rural and remote areas. However, complex layers of decisions are required to critically assess, harness and optimise technologies to best suit the needs of users, and to utilise teaching and learning techniques that best match the technologies and participant needs. Getting these elements — needs, technology and learning technique — right, nevertheless offers extraordinary opportunities. Sound decisions and good practices should enable state-wide and specialist law and justice-related services interested in improving their engagement with RRR communities to dramatically improve the reach and quality of outcomes, not only for distant participants but the spectrum of stakeholders.

Industry-based learning

Research from the fields of adult learning, workplace education, professional development, organisational learning and co-operative education are drawn on to identify elements that should be considered in the design, implementation, management and sustained improvement of co-op programs. Each work placement is unique meld of stakeholders, the job and the organisational context. Key factors that promote learning in the workplace include the engagement of the work place supervisor in the student's professional development; the learning environment within the organisation; and the student's own motivation, abilities and learning orientation: It is proposed that information systems delivering co-op programs need to manage: (a) the development and performance of industry partnerships, (b) the relationships between key stakeholders; and (c) the professional skills development and learning. © 2009 Springer-Verlag US.

Supervised learning for labelling human body with attached props

 In this research, a novel method for generating training data of human postures with attached objects is proposed. The results has shown a significant increase in body-part classification accuracy for subjects with props from 60% to 94% using the generated image set

Learning clinical skills during bedside teaching encounters in general practice: a video-observational study with insights from activity theory

Purpose – This paper aims to explore how opportunities for learning clinical skills are negotiated within bedside teaching encounters (BTEs). Bedside teaching, within the medical workplace, is considered essential for helping students develop their clinical skills. Design/methodology/approach – An audio and/or video observational study examining seven general practice BTEs was undertaken. Additionally, audio-recorded, semi-structured interviews were conducted with participants. All data were transcribed. Data analysis comprised Framework Analysis informed by Engeström’s Cultural Historical Activity Theory. Findings – BTEs can be seen to offer many learning opportunities for clinical skills. Learning opportunities are negotiated by the participants in each BTE, with patients, doctors and students playing different roles within and across the BTEs. Tensions emerged within and between nodes and across two activity systems. Research limitations/implications – Negotiation of clinical skills learning opportunities involved shifts in the use of artefacts, roles and rules of participation, which were tacit, dynamic and changing. That learning is constituted in the activity implies that students and teachers cannot be fully prepared for BTEs due to their emergent properties. Engaging doctors, students and patients in refecting on tensions experienced and the factors that infuence judgements in BTEs may be a useful frst step in helping them better manage the roles and responsibilities therein. Originality/value – The paper makes an original contribution to the literature by highlighting the tensions inherent in BTEs and how the negotiation of roles and division of labour whilst juggling two interacting activity systems create or inhibit opportunities for clinical skills learning. This has signifcant implications for how BTEs are conceptualised.

What are the perceived learning needs of Australian general practice registrars for quality prescribing?

BACKGROUND: Little is known about the perceived learning needs of Australian general practice (GP) registrars in relation to the quality use of medicines (QUM) or the difficulties experienced when learning to prescribe. This study aimed to address this gap. METHODS: GP registrars' perceived learning needs were investigated through an online national survey, interviews and focus groups. Medical educators' perceptions were canvassed in semi-structured interviews in order to gain a broader perspective of the registrars' needs. Qualitative data analysis was informed by a systematic framework method involving a number of stages. Survey data were analysed descriptively. RESULTS: The two most commonly attended QUM educational activities took place in the workplace and through regional training providers. Outside of these structured educational activities, registrars learned to prescribe mainly through social and situated means. Difficulties encountered by GP registrars included the transition from hospital prescribing to prescribing in the GP context, judging how well they were prescribing and identifying appropriate and efficient sources of information at the point of care. CONCLUSIONS: GP registrars learn to prescribe primarily and opportunistically in the workplace. Despite many resources being expended on the provision of guidelines, decision-support systems and training, GP registrars expressed difficulties related to QUM. Ways of easing the transition into GP and of managing the information 'overload' related to medicines (and prescribing) in an evidence-guided, efficient and timely manner are needed. GP registrars should be provided with explicit feedback about the process and outcomes of prescribing decisions, including the use of audits, in order to improve their ability to judge their own prescribing.

Multiple task transfer learning with small sample sizes

Prognosis, such as predicting mortality, is common in medicine. When confronted with small numbers of samples, as in rare medical conditions, the task is challenging. We propose a framework for classification with data with small numbers of samples. Conceptually, our solution is a hybrid of multi-task and transfer learning, employing data samples from source tasks as in transfer learning, but considering all tasks together as in multi-task learning. Each task is modelled jointly with other related tasks by directly augmenting the data from other tasks. The degree of augmentation depends on the task relatedness and is estimated directly from the data. We apply the model on three diverse real-world data sets (healthcare data, handwritten digit data and face data) and show that our method outperforms several state-of-the-art multi-task learning baselines. We extend the model for online multi-task learning where the model parameters are incrementally updated given new data or new tasks. The novelty of our method lies in offering a hybrid multi-task/transfer learning model to exploit sharing across tasks at the data-level and joint parameter learning.

Collaborating differently on different topics: a multi-relational approach to multi-task learning

Multi-task learning offers a way to benefit from synergy of multiple related prediction tasks via their joint modeling. Current multi-task techniques model related tasks jointly, assuming that the tasks share the same relationship across features uniformly. This assumption is seldom true as tasks may be related across some features but not others. Addressing this problem, we propose a new multi-task learning model that learns separate task relationships along different features. This added flexibility allows our model to have a finer and differential level of control in joint modeling of tasks along different features. We formulate the model as an optimization problem and provide an efficient, iterative solution. We illustrate the behavior of the proposed model using a synthetic dataset where we induce varied feature-dependent task relationships: positive relationship, negative relationship, no relationship. Using four real datasets, we evaluate the effectiveness of the proposed model for many multi-task regression and classification problems, and demonstrate its superiority over other state-of-the-art multi-task learning models

Prediciton of emergency events: a multi-task multi-label learning approach

Prediction of patient outcomes is critical to plan resources in an hospital emergency department. We present a method to exploit longitudinal data from Electronic Medical Records (EMR), whilst exploiting multiple patient outcomes. We divide the EMR data into segments where each segment is a task, and all tasks are associated with multiple patient outcomes over a 3, 6 and 12 month period. We propose a model that learns a prediction function for each task-label pair, interacting through two subspaces: the first subspace is used to impose sharing across all tasks for a given label. The second subspace captures the task-specific variations and is shared across all the labels for a given task. The proposed model is formulated as an iterative optimization problems and solved using a scalable and efficient Block co-ordinate descent (BCD) method. We apply the proposed model on two hospital cohorts - Cancer and Acute Myocardial Infarction (AMI) patients collected over a two year period from a large hospital emergency department. We show that the predictive performance of our proposed models is significantly better than those of several state-of-the-art multi-task and multi-label learning methods.

Learning entry profiles of children with autism from multivariate treatment information using restricted boltzmann machines

Entry profiles can be generated before children with Autism Spectrum Disorders (ASD) begin to traverse an intervention program. They can help evaluate the progress of each child on the dedicated syllabus in addition to enabling narrowing down the best intervention course over time. However, the traits of ASD are expressed in different ways in every individual affected. The resulting spectrum nature of the disorder makes it challenging to discover profiles of children with ASD. Using data from 491 children, traversing the syllabus of a comprehensive intervention program on iPad called TOBY Playpad, we learn the entry profiles of the children based on their age, sex and performance on their first skills of the syllabus. Mixed-variate restricted Boltzmann machines allow us to integrate the heterogeneous data into one model making it a suitable technique. The data based discovery of entry profiles may assist in developing systems that can automatically suggest best suitable paths through the syllabus by clustering the children based on the characteristics they present at the beginning of the program. This may open the pathway for personalised intervention.

A systematic design of interval type-2 fuzzy logic system using extreme learning machine for electricity load demand forecasting

This paper presents a novel design of interval type-2 fuzzy logic systems (IT2FLS) by utilizing the theory of extreme learning machine (ELM) for electricity load demand forecasting. ELM has become a popular learning algorithm for single hidden layer feed-forward neural networks (SLFN). From the functional equivalence between the SLFN and fuzzy inference system, a hybrid of fuzzy-ELM has gained attention of the researchers. This paper extends the concept of fuzzy-ELM to an IT2FLS based on ELM (IT2FELM). In the proposed design the antecedent membership function parameters of the IT2FLS are generated randomly, whereas the consequent part parameters are determined analytically by the Moore-Penrose pseudo inverse. The ELM strategy ensures fast learning of the IT2FLS as well as optimality of the parameters. Effectiveness of the proposed design of IT2FLS is demonstrated with the application of forecasting nonlinear and chaotic data sets. Nonlinear data of electricity load from the Australian National Electricity Market for the Victoria region and from the Ontario Electricity Market are considered here. The proposed model is also applied to forecast Mackey-glass chaotic time series data. Comparative analysis of the proposed model is conducted with some traditional models such as neural networks (NN) and adaptive neuro fuzzy inference system (ANFIS). In order to verify the structure of the proposed design of IT2FLS an alternate design of IT2FLS based on Kalman filter (KF) is also utilized for the comparison purposes.