Library and information science education 2.0 : guiding principles and models of best practice. Interview with Huan Vo-Tran.

In 2005, Stephen Abram, vice president of Innovation at SirsiDynix, challenged library and information science (LIS) professionals to start becoming “librarian 2.0.” In the last few years, discussion and debate about the “core competencies” needed by librarian 2.0 have appeared in the “biblioblogosphere” (blogs written by LIS professionals). However, beyond these informal blog discussions few systematic and empirically based studies have taken place. A project funded by the Australian Learning and Teaching Council fills this gap. The project identifies the key skills, knowledge, and attributes required by “librarian 2.0.” Eighty-one members of the Australian LIS profession participated in a series of focus groups. Eight themes emerged as being critical to “librarian 2.0”: technology, communication, teamwork, user focus, business savvy, evidence based practice, learning and education, and personal traits. Guided by these findings interviews with 36 LIS educators explored the current approaches used within contemporary LIS education to prepare graduates to become “librarian 2.0”. This video presents an example of ‘great practice’ in current LIS education as it strives to foster web 2.0 professionals.

Library and information science education 2.0 : guiding principles and models of best practice ALTC Fellowship Report

This ALTC Teaching Fellowship aimed to establish Guiding Principles for Library and Information Science Education 2.0. The aim was achieved by (i) identifying the current and anticipated skills and knowledge required by successful library and information science (LIS) professionals in the age of web 2.0 (and beyond), (ii) establishing the current state of LIS education in Australia in supporting the development of librarian 2.0, and in doing so, identify models of best practice. The fellowship has contributed to curriculum renewal in the LIS profession. It has helped to ensure that LIS education in Australia continues to meet the changing skills and knowledge requirements of the profession it supports. It has also provided a vehicle through which LIS professionals and LIS educators may find opportunities for greater collaboration and more open communication. This will help bridge the gap between LIS theory and practice and will foster more authentic engagement between LIS education and other parts of the LIS industry in the education of the next generation of professionals. Through this fellowship the LIS discipline has become a role model for other disciplines who will be facing similar issues in the coming years. Eighty-one members of the Australian LIS profession participated in a series of focus groups exploring the current and anticipated skills and knowledge needed by the LIS professional in the web 2.0 world and beyond. Whilst each focus group tended to draw on specific themes of interest to that particular group of people, there was a great deal of common ground. Eight key themes emerged: technology, learning and education, research or evidence-based practice, communication, collaboration and team work, user focus, business savvy and personal traits. It was acknowledged that the need for successful LIS professionals to possess transferable skills and interpersonal attributes was not new. It was noted however that the speed with which things are changing in the web 2.0 world was having a significant impact and that this faster pace is placing a new and unexpected emphasis on the transferable skills and knowledge. It was also acknowledged that all librarians need to possess these skills, knowledge and attributes and not just the one or two role models who lead the way. The most interesting finding however was that web 2.0, library 2.0 and librarian 2.0 represented a ‘watershed’ for the LIS profession. Almost all the focus groups spoke about how they are seeing and experiencing a culture change in the profession. Librarian 2.0 requires a ‘different mindset or attitude’. The Levels of Perspective model by Daniel Kim provides one lens by which to view this finding. The focus group findings suggest that we are witnessing a re-awaking of the Australian LIS profession as it begins to move towards the higher levels of Kim’s model (ie mental models, vision). Thirty-six LIS educators participated in telephone interviews aimed at exploring the current state of LIS education in supporting the development of librarian 2.0. Skills and knowledge of LIS professionals in a web 2.0 world that were identified and discussed by the LIS educators mirrored those highlighted in the focus group discussions with LIS professionals. Similarly it was noted that librarian 2.0 needed a focus less on skills and knowledge and more on attitude. However, whilst LIS professionals felt that there was a paradigm shift within the profession. LIS educators did not speak with one voice on this matter with quite a number of the educators suggesting that this might be ‘overstating it a bit’. This study provides evidence for “disparate viewpoints” (Hallam, 2007) between LIS educators and LIS professionals that can have a significant implications for the future of not just LIS professional education specifically but for the profession generally. Library and information science education 2.0: guiding principles and models of best practice 1 Inviting the LIS academics to discuss how their teaching and learning activities support the development of librarian 2.0 was a core part of the interviews conducted. The strategies used and the challenges faced by LIS educators in developing their teaching and learning approaches to support the formation of librarian 2.0 are identified and discussed. A core part of the fellowship was the identification of best practice examples on how LIS educators were developing librarian 2.0. Twelve best practice examples were identified. Each educator was recorded discussing his or her approach to teaching and learning. Videos of these interviews are available via the Fellowship blog at .The LIS educators involved in making the videos felt uncomfortable with the term ‘best practice’. Many acknowledged that there simply seeking to do the best by their students and that there was always room for improvement. For this reason these videos are offered as examples of “great practice”. The videos are a tool for other educators to use, regardless of discipline, in developing their teaching and learning approaches to supporting web 2.0 professionals. It has been argued that the main purpose of professional education is transformation (Dall’ Alba, 2009; Dall’Alba & Barnacle, 2007). As such professional education should focus not just on skills and knowledge acquisition but also on helping students to develop ways of being the professionals in question (ie LIS professionals, teachers, lawyers, engineers).The aim of this fellowship was to establish Guidelines for Library and Information Science Education 2.0 it has however become apparent that at this point in time it is not yet possible to fulfil this aim. The fellowship has clearly identified skills and knowledge needed by the LIS professional in web 2.0 world (and beyond). It has also identified examples of ‘great practice’ by LIS educators as they endeavour to develop LIS professionals who will be successful in a web 20 world. The fellowship however has also shown that the LIS profession is currently undergoing significant attitudinal and conceptual change. Consequently, before a philosophy of LIS education 2.0 can be expressed, the Australian LIS profession must first explore and articulate what it means to be an LIS professional in the 21st century (ie a world of web 2.0 and beyond). In short, the LIS profession in Australia must take stock not of “what we know and can do” but on “who we are becoming” (Dall’Alba, 2009, p 34).

Cross likelihood ratio based speaker clustering using eigenvoice models

This paper proposes the use of eigenvoice modeling techniques with the Cross Likelihood Ratio (CLR) as a criterion for speaker clustering within a speaker diarization system. The CLR has previously been shown to be a robust decision criterion for speaker clustering using Gaussian Mixture Models. Recently, eigenvoice modeling techniques have become increasingly popular, due to its ability to adequately represent a speaker based on sparse training data, as well as an improved capture of differences in speaker characteristics. This paper hence proposes that it would be beneficial to capitalize on the advantages of eigenvoice modeling in a CLR framework. Results obtained on the 2002 Rich Transcription (RT-02) Evaluation dataset show an improved clustering performance, resulting in a 35.1% relative improvement in the overall Diarization Error Rate (DER) compared to the baseline system.

Small animal bone healing models : standards, tips, and pitfalls results of a consensus meeting

Small animal fracture models have gained increasing interest in fracture healing studies. To achieve standardized and defined study conditions, various variables must be carefully controlled when designing fracture healing experiments in mice or rats. The strain, age and sex of the animals may influence the process of fracture healing. Furthermore, the choice of the fracture fixation technique depends on the questions addressed, whereby intra- and extramedullary implants as well as open and closed surgical approaches may be considered. During the last few years, a variety of different, highly sophisticated implants for fracture fixation in small animals have been developed. Rigid fixation with locking plates or external fixators results in predominantly intramembranous healing in both mice and rats. Locking plates, external fixators, intramedullary screws, the locking nail and the pin-clip device allow different degrees of stability resulting in various amounts of endochondral and intramembranous healing. The use of common pins that do not provide rotational and axial stability during fracture stabilization should be discouraged in the future. Analyses should include at least biomechanical and histological evaluations, even if the focus of the study is directed towards the elucidation of molecular mechanisms of fracture healing using the largely available spectrum of antibodies and gene-targeted animals to study molecular mechanisms of fracture healing. This review discusses distinct requirements for the experimental setups as well as the advantages and pitfalls of the different fixation techniques in rats and mice.

Higher-Order spectra of nonlinear polynomial models for Chua's circuit

Polynomial models are shown to simulate accurately the quadratic and cubic nonlinear interactions (e.g. higher-order spectra) of time series of voltages measured in Chua's circuit. For circuit parameters resulting in a spiral attractor, bispectra and trispectra of the polynomial model are similar to those from the measured time series, suggesting that the individual interactions between triads and quartets of Fourier components that govern the process dynamics are modeled accurately. For parameters that produce the double-scroll attractor, both measured and modeled time series have small bispectra, but nonzero trispectra, consistent with higher-than-second order nonlinearities dominating the chaos.

A proposed role for models and simulations in management research

Overall, computer models and simulations have a rather disappointing record within the management sciences as a tool for predicting the future. Social and market environments can be influenced by an overwhelming number of variables, and it is therefore difficult to use computer models to make forecasts or to test hypotheses concerning the relationship between individual behaviours and macroscopic outcomes. At the same time, however, advocates of computer models argue that they can be used to overcome the human mind's inability to cope with several complex variables simultaneously or to understand concepts that are highly counterintuitive. This paper seeks to bridge the gap between these two perspectives by suggesting that management research can indeed benefit from computer models by using them to formulate fruitful hypotheses.

An infinite-horizon robust filter for uncertain hidden Markov Models with conditional relative entropy constraints

We consider a robust filtering problem for uncertain discrete-time, homogeneous, first-order, finite-state hidden Markov models (HMMs). The class of uncertain HMMs considered is described by a conditional relative entropy constraint on measures perturbed from a nominal regular conditional probability distribution given the previous posterior state distribution and the latest measurement. Under this class of perturbations, a robust infinite horizon filtering problem is first formulated as a constrained optimization problem before being transformed via variational results into an unconstrained optimization problem; the latter can be elegantly solved using a risk-sensitive information-state based filtering.

Bayesian models for the determination of resonant frequencies in a DI diesel engine

A time series method for the determination of combustion chamber resonant frequencies is outlined. This technique employs the use of Markov-chain Monte Carlo (MCMC) to infer parameters in a chosen model of the data. The development of the model is included and the resonant frequency is characterised as a function of time. Potential applications for cycle-by-cycle analysis are discussed and the bulk temperature of the gas and the trapped mass in the combustion chamber are evaluated as a function of time from resonant frequency information.

Emergency health services : demand and service delivery models. Monograph 1: literature review and activity trends

Emergency Health Services (EHS), encompassing hospital-based Emergency Departments (ED) and pre-hospital ambulance services, are a significant and high profile component of Australia’s health care system and congestion of these, evidenced by physical overcrowding and prolonged waiting times, is causing considerable community and professional concern. This concern relates not only to Australia’s capacity to manage daily health emergencies but also the ability to respond to major incidents and disasters. EHS congestion is a result of the combined effects of increased demand for emergency care, increased complexity of acute health care, and blocked access to ongoing care (e.g. inpatient beds). Despite this conceptual understanding there is a lack of robust evidence to explain the factors driving increased demand, or how demand contributes to congestion, and therefore public policy responses have relied upon limited or unsound information. The Emergency Health Services Queensland (EHSQ) research program proposes to determine the factors influencing the growing demand for emergency health care and to establish options for alternative service provision that may safely meet patient’s needs. The EHSQ study is funded by the Australian Research Council (ARC) through its Linkage Program and is supported financially by the Queensland Ambulance Service (QAS). This monograph is part of a suite of publications based on the research findings that examines the existing literature, and current operational context. Literature was sourced using standard search approaches and a range of databases as well as a selection of articles cited in the reviewed literature. Public sources including the Australian Institute of Health and Welfare (AIHW), the Council of Ambulance Authorities (CAA) Annual Reports, Australian Bureau of Statistics (ABS) and Department of Health and Ageing (DoHA) were examined for trend data across Australia.

Query expansion using term relationships in language models for information retrieval

Language Modeling (LM) has been successfully applied to Information Retrieval (IR). However, most of the existing LM approaches only rely on term occurrences in documents, queries and document collections. In traditional unigram based models, terms (or words) are usually considered to be independent. In some recent studies, dependence models have been proposed to incorporate term relationships into LM, so that links can be created between words in the same sentence, and term relationships (e.g. synonymy) can be used to expand the document model. In this study, we further extend this family of dependence models in the following two ways: (1) Term relationships are used to expand query model instead of document model, so that query expansion process can be naturally implemented; (2) We exploit more sophisticated inferential relationships extracted with Information Flow (IF). Information flow relationships are not simply pairwise term relationships as those used in previous studies, but are between a set of terms and another term. They allow for context-dependent query expansion. Our experiments conducted on TREC collections show that we can obtain large and significant improvements with our approach. This study shows that LM is an appropriate framework to implement effective query expansion.

Using Bayesian methods for the estimation of uncertainty in complex statistical models

The research objectives of this thesis were to contribute to Bayesian statistical methodology by contributing to risk assessment statistical methodology, and to spatial and spatio-temporal methodology, by modelling error structures using complex hierarchical models. Specifically, I hoped to consider two applied areas, and use these applications as a springboard for developing new statistical methods as well as undertaking analyses which might give answers to particular applied questions. Thus, this thesis considers a series of models, firstly in the context of risk assessments for recycled water, and secondly in the context of water usage by crops. The research objective was to model error structures using hierarchical models in two problems, namely risk assessment analyses for wastewater, and secondly, in a four dimensional dataset, assessing differences between cropping systems over time and over three spatial dimensions. The aim was to use the simplicity and insight afforded by Bayesian networks to develop appropriate models for risk scenarios, and again to use Bayesian hierarchical models to explore the necessarily complex modelling of four dimensional agricultural data. The specific objectives of the research were to develop a method for the calculation of credible intervals for the point estimates of Bayesian networks; to develop a model structure to incorporate all the experimental uncertainty associated with various constants thereby allowing the calculation of more credible credible intervals for a risk assessment; to model a single day’s data from the agricultural dataset which satisfactorily captured the complexities of the data; to build a model for several days’ data, in order to consider how the full data might be modelled; and finally to build a model for the full four dimensional dataset and to consider the timevarying nature of the contrast of interest, having satisfactorily accounted for possible spatial and temporal autocorrelations. This work forms five papers, two of which have been published, with two submitted, and the final paper still in draft. The first two objectives were met by recasting the risk assessments as directed, acyclic graphs (DAGs). In the first case, we elicited uncertainty for the conditional probabilities needed by the Bayesian net, incorporated these into a corresponding DAG, and used Markov chain Monte Carlo (MCMC) to find credible intervals, for all the scenarios and outcomes of interest. In the second case, we incorporated the experimental data underlying the risk assessment constants into the DAG, and also treated some of that data as needing to be modelled as an ‘errors-invariables’ problem [Fuller, 1987]. This illustrated a simple method for the incorporation of experimental error into risk assessments. In considering one day of the three-dimensional agricultural data, it became clear that geostatistical models or conditional autoregressive (CAR) models over the three dimensions were not the best way to approach the data. Instead CAR models are used with neighbours only in the same depth layer. This gave flexibility to the model, allowing both the spatially structured and non-structured variances to differ at all depths. We call this model the CAR layered model. Given the experimental design, the fixed part of the model could have been modelled as a set of means by treatment and by depth, but doing so allows little insight into how the treatment effects vary with depth. Hence, a number of essentially non-parametric approaches were taken to see the effects of depth on treatment, with the model of choice incorporating an errors-in-variables approach for depth in addition to a non-parametric smooth. The statistical contribution here was the introduction of the CAR layered model, the applied contribution the analysis of moisture over depth and estimation of the contrast of interest together with its credible intervals. These models were fitted using WinBUGS [Lunn et al., 2000]. The work in the fifth paper deals with the fact that with large datasets, the use of WinBUGS becomes more problematic because of its highly correlated term by term updating. In this work, we introduce a Gibbs sampler with block updating for the CAR layered model. The Gibbs sampler was implemented by Chris Strickland using pyMCMC [Strickland, 2010]. This framework is then used to consider five days data, and we show that moisture in the soil for all the various treatments reaches levels particular to each treatment at a depth of 200 cm and thereafter stays constant, albeit with increasing variances with depth. In an analysis across three spatial dimensions and across time, there are many interactions of time and the spatial dimensions to be considered. Hence, we chose to use a daily model and to repeat the analysis at all time points, effectively creating an interaction model of time by the daily model. Such an approach allows great flexibility. However, this approach does not allow insight into the way in which the parameter of interest varies over time. Hence, a two-stage approach was also used, with estimates from the first-stage being analysed as a set of time series. We see this spatio-temporal interaction model as being a useful approach to data measured across three spatial dimensions and time, since it does not assume additivity of the random spatial or temporal effects.

Velocity-jump models with crowding effects

Velocity jump processes are discrete random walk models that have many applications including the study of biological and ecological collective motion. In particular, velocity jump models are often used to represent a type of persistent motion, known as a “run and tumble”, which is exhibited by some isolated bacteria cells. All previous velocity jump processes are non-interacting, which means that crowding effects and agent-to-agent interactions are neglected. By neglecting these agent-to-agent interactions, traditional velocity jump models are only applicable to very dilute systems. Our work is motivated by the fact that many applications in cell biology, such as wound healing, cancer invasion and development, often involve tissues that are densely packed with cells where cell-to-cell contact and crowding effects can be important. To describe these kinds of high cell density problems using a velocity jump process we introduce three different classes of crowding interactions into a one-dimensional model. Simulation data and averaging arguments lead to a suite of continuum descriptions of the interacting velocity jump processes. We show that the resulting systems of hyperbolic partial differential equations predict the mean behavior of the stochastic simulations very well.

Clustering of web users using the tensor decomposed models

We propose to use the Tensor Space Modeling (TSM) to represent and analyze the user’s web log data that consists of multiple interests and spans across multiple dimensions. Further we propose to use the decomposition factors of the Tensors for clustering the users based on similarity of search behaviour. Preliminary results show that the proposed method outperforms the traditional Vector Space Model (VSM) based clustering.

Understanding business process models : the costs and benefits of structuredness

Previous research has put forward a number of properties of business process models that have an impact on their understandability. Two such properties are compactness and(block-)structuredness. What has not been sufficiently appreciated at this point is that these desirable properties may be at odds with one another. This paper presents the results of a two-pronged study aimed at exploring the trade-off between compactness and structuredness of process models. The first prong of the study is a comparative analysis of the complexity of a set of unstructured process models from industrial practice and of their corresponding structured versions. The second prong is an experiment wherein a cohort of students was exposed to semantically equivalent unstructured and structured process models. The key finding is that structuredness is not an absolute desideratum vis-a-vis for process model understandability. Instead, subtle trade-offs between structuredness and other model properties are at play.